Causal Analysis in Theory and Practice

April 13, 2017

Causal Inference with Directed Graphs – Seminar

Filed under: Announcement — Judea Pearl @ 5:27 am


We would like to promote another causal inference short course. This 2-day seminar won the 2013 Causality in Statistics Education Award, given by the American Statistical Association. See the details below for more information:

Causal Inference with Directed Graphs: This seminar offers an applied introduction to directed acyclic graphs (DAGs) for causal inference. DAGs are a powerful new tool for understanding and resolving causal problems in empirical research. DAGs are useful for social and biomedical researchers, business and policy analysts who want to draw causal inferences from non-experimental data. The chief advantage of DAGs is that they are “algebra-free,” relying instead on intuitive yet rigorous graphical rules.

Instructor: Felix Elwert, Ph.D.

Who should attend: If you want to understand under what circumstances you can draw causal inferences from non-experimental data, this course is for you. Participants should have a good working knowledge of multiple regression and basic concepts of probability. Some prior exposure to causal inference (counterfactuals, propensity scores, instrumental variables analysis) will be helpful but is not essential.

Tuition: The fee of $995.00 includes all seminar materials.

Date/Location: The seminar meets Friday, April 28 and Saturday, April 29 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.

Details and registration:

April 8, 2017

Causal Inference Short Course at Harvard

Filed under: Announcement — Judea Pearl @ 2:31 am


We’ve received news that Harvard is offering a short course on causal inference that may be of interest to readers of this blog. See the details below for more information:

An Introduction to Causal Inference: This 5-day course introduces concepts and methods for causal inference from observational data. Upon completion of the course, participants will be prepared to further explore the causal inference literature. Topics covered include the g-formula, inverse probability weighting of marginal structural models, g-estimation of structural nested models, causal mediation analysis, and methods to handle unmeasured confounding. The last day will end with a “capstone” open Q&A session.

Instructors: Miguel Hernán, Judith Lok, James Robins, Eric Tchetgen Tchetgen & Tyler VanderWeele

Prerequisites: Participants are expected to be familiar with basic concepts in epidemiology and biostatistics, including linear and logistic regression and survival analysis techniques.

Tuition: $450/person, to be paid at the time of registration. Tuition will be waived for up to 2 students with primary affiliation at an institution in a developing country.

Date/Location: June 12-16, 2017 at the Harvard T.H. Chan School of Public Health

Details and registration:

February 22, 2017

Winter-2017 Greeting from UCLA Causality Blog

Filed under: Announcement,Causal Effect,Economics,Linear Systems — bryantc @ 6:03 pm

Dear friends in causality research,

In this brief greeting I would like to first call attention to an approaching deadline and then discuss a couple of recent articles.

Causality in Education Award – March 1, 2017

We are informed that the deadline for submitting a nomination for the ASA Causality in Statistics Education Award is March 1, 2017. For purpose, criteria and other information please see .

The next issue of the Journal of Causal Inference (JCI) is schedule to appear March, 2017. See

MY contribution to this issue includes a tutorial paper entitled: “A Linear ‘Microscope’ for Interventions and Counterfactuals”. An advance copy can be viewed here:

Overturning Econometrics Education (or, do we need a “causal interpretation”?)

My attention was called to a recent paper by Josh Angrist and Jorn-Steffen Pischke titled: “Undergraduate econometrics instruction” (A NBER working paper)

This paper advocates a pedagogical paradigm shift that has methodological ramifications beyond econometrics instruction; As I understand it, the shift stands contrary to the traditional teachings of causal inference, as defined by Sewall Wright (1920), Haavelmo (1943), Marschak (1950), Wold (1960), and other founding fathers of econometrics methodology.

In a nut shell, Angrist and Pischke  start with a set of favorite statistical routines such as IV, regression, differences-in-differences among others, and then search for “a set of control variables needed to insure that the regression-estimated effect of the variable of interest has a causal interpretation”. Traditional causal inference (including economics) teaches us that asking whether the output of a statistical routine “has a causal interpretation” is the wrong question to ask, for it misses the direction of the analysis. Instead, one should start with the target causal parameter itself, and asks whether it is ESTIMABLE (and if so how), be it by IV, regression, differences-in-differences, or perhaps by some new routine that is yet to be discovered and ordained by name. Clearly, no “causal interpretation” is needed for parameters that are intrinsically causal; for example, “causal effect”, “path coefficient”, “direct effect”, “effect of treatment on the treated”, or “probability of causation”.

In practical terms, the difference between the two paradigms is that estimability requires a substantive model while interpretability appears to be model-free. A model exposes its assumptions explicitly, while statistical routines give the deceptive impression that they run assumptions-free (hence their popular appeal). The former lends itself to judgmental and statistical tests, the latter escapes such scrutiny.

In conclusion, if an educator needs to choose between the “interpretability” and “estimability” paradigms, I would go for the latter. If traditional econometrics education
is tailored to support the estimability track, I do not believe a paradigm shift is warranted towards an “interpretation seeking” paradigm as the one proposed by Angrist and Pischke,

I would gladly open this blog for additional discussion on this topic.

I tried to post a comment on NBER (National Bureau of Economic Research), but was rejected for not being an approved “NBER family member”. If any of our readers is a “”NBER family member” feel free to post the above. Note: “NBER working papers are circulated for discussion and comment purposes.” (page 1).

September 15, 2016

Summer-end Greeting from the UCLA Causality Blog

Filed under: Uncategorized — bryantc @ 4:39 am

Dear friends in causality research,
This greeting from UCLA Causality blog contains news and discussion on the following topics:

1. Reflections on 2016 JSM meeting.
2. The question of equivalent representations.
3. Simpson’s Paradox (Comments on four recent papers)
4. News concerning Causal Inference Primer
5. New books, blogs and other frills.

1. Reflections on JSM-2016
For those who missed the JSM 2016 meeting, my tutorial slides can be viewed here:

As you can see, I argue that current progress in causal inference should be viewed as a major paradigm shift in the history of statistics and, accordingly, nuances and disagreements are merely linguistic realignments within a unified framework. To support this view, I chose for discussion six specific achievements (called GEMS) that should make anyone connected with causal analysis proud, empowered, and mighty motivated.

The six gems are:
1. Policy Evaluation (Estimating “Treatment Effects”)
2. Attribution Analysis (Causes of Effects)
3. Mediation Analysis (Estimating Direct and Indirect Effects)
4. Generalizability (Establishing External Validity)
5. Coping with Selection Bias
6. Recovering from Missing Data

I hope you enjoy the slides and appreciate the gems.

2. The question of equivalent representations
One challenging question that came up from the audience at JSM concerned the unification of the graphical and potential-outcome frameworks. “How can two logically equivalent representations be so different in actual use?”. I elaborate on this question in a separate post titled “Logically equivalent yet way too different.”

3. Simpson’s Paradox: The riddle that would not die
(Comments on four recent papers)
If you search Google for “Simpson’s paradox”, as I did yesterday, you would get 111,000 results, more than any other statistical paradox that I could name. What elevates this innocent reversal of associations to “paradoxical” status, and why it has captured the fascination of statisticians, mathematicians and philosophers for over a century are questions that we discussed at length on this (and other) blogs. The reason I am back to this topic is the publication of four recent papers that give us a panoramic view at how the understanding of causal reasoning has progressed in communities that do not usually participate in our discussions.

4. News concerning Causal Inference – A Primer
We are grateful to Jim Grace for his in-depth review on Amazon:

For those of you awaiting the solutions to the study questions in the Primer, I am informed that the Solution Manual is now available (to instructors) from Wiley. To obtain a copy, see page 2 of: However, rumor has it that a quicker way to get it is through your local Wiley representative, at

If you encounter difficulties, please contact us at and we will try to help. Readers tell me that the solutions are more enlightening than the text. I am not surprised, there is nothing more invigorating than seeing a non-trivial problem solved from A to Z.

5. New books, blogs and other frills
We are informed that a new book by Joseph Halpern, titled “Actual Causality”, is available now from MIT Press. ( Readers familiar with Halpern’s fundamental contributions to causal reasoning will not be surprised to find here a fresh and comprehensive solution to the age-old problem of actual causality. Not to be missed.

Adam Kelleher writes about an interesting math-club and causal-minded blog that he is orchestrating. See his post,

Glenn Shafer just published a review paper: “A Mathematical Theory of Evidence turn 40” celebrating the 40th anniversary of the publication of his 1976 book “A Mathematical Theory of Evidence” I have enjoyed reading this article for nostalgic reasons, reminding me of the stormy days in the 1980’s, when everyone was arguing for another calculus of evidential reasoning. My last contribution to that storm, just before sailing off to causality land, was this paper: Section 10 of Shafer’s article deals with his 1996 book “The Art of Causal Conjecture” My thought: Now, that the causal inference field has matured, perhaps it is time to take another look at the way Shafer views causation.

Wishing you a super productive Fall season.

J. Pearl

September 12, 2016

Logically equivalent yet way too different

Filed under: Uncategorized — bryantc @ 2:50 am

Contributor: Judea Pearl

In comparing the tradeoffs between the structural and potential outcome frameworks, I often state that the two are logically equivalent yet poles apart in terms of transparency and computational efficiency. (See Slide #34 of the JSM tutorial). Indeed, anyone who examines how the two frameworks solve a specific problem from begining to end (See, e.g., Slides #35-36 ) would find the differences astonishing.

The question naturally arises: How can two equivalent frameworks differ so substantially in actual use.

The answer is that epistemic equivalence does not mean representational equivalence. Two representations of the same information may highlight different aspects of the problem and thus differ substantially in how easy it is to solve a given problem.  This is a recurrent theme in complexity analysis, but is not generally appreciated outside computer science. We saw it in our discussions with Guido Imbens who could not accept the fact that the use of graphical models is a mathematical necessity not just a matter of taste. (

The examples usually cited in complexity analysis are combinatorial problems whose solution times depend critically on the initial representation. I hesitated from bringing up these examples, fearing that they will not be compelling to readers on this blog who are more familiar with classical mathematics.

Last week I stumbled upon a very simple example that demonstrates representational differences in no ambiguous terms; I would like to share it with readers.

Consider the age-old problem of finding a solution to an algebraic equation, say
y(x) = x3 + ax2 + bx + c = 0

This is a tough problem for those of us who do not remember Tartalia’s solution of the cubic.  (It can be made much tougher once we go to quintic equation.)

But there are many syntactic ways of representing the same function y(x) . Here is one equivalent representation:
y(x) = x(x2+ax) + b(x+c/b) = 0
and here is another:
y(x) = (x-x1)(x-x2)(x-x3) = 0,
where x1, x2, and x3 are some functions of a, b, c.

The last representation permits an immediate solution, which is:
x=x1, x=x2, x=x3.

The example may appear trivial, and some may even call it cheating, saying that finding x1, x2, and x3 is as hard as solving the original problem. This is true, but the purpose of the example was not to produce an easy solution to the cubic. The purpose was to demonstrate that different syntactic ways of representing the same information (i.e., the same polynomial) may lead to substantial differences in the complexity of computing an answer to a query (i.e., find a root).

A preferred representation is one that makes certain desirable aspects of the problem explicit, thus facilitating a speedy solution. Complexity theory is full of such examples.

Note that the complexity is query-dependent. Had our goal been to find a value x that makes the polynomial y(x) equal 4, not zero, the representation above y(x) = (x-x1)(x-x2)(x-x3) would offer no help at all. For this query, the representation
y(x) = (x-z1)(x-z2)(x-z3) + 4  
would yield an immediate solution
x=z1, x=z2, x=z3,
where z1, z2, and z3 are the roots of another polynomial:
x3 + ax2 + bx + (c-4) = 0

This simple example demonstrates nicely the principle that makes graphical models more efficient than alternative representations of the same causal information, say a set of ignorability assumptions. What makes graphical models efficient is the fact that they make explicit the logical ramifications of the conditional-independencies conveyed by the model. Deriving those ramifications by algebraic or logical means takes substantially more work. (See for the logic of counterfactual independencies)

A typical example of how nasty such derivations can get is given in Heckman and Pinto’s paper on “Causal Inference after Haavelmo” (Econometric Theory, 2015). Determined to avoid graphs at all cost, Heckman and Pinto derived conditional independence relations directly from Dawid’s axioms and the Markov condition (See The results are pages upon pages of derivations of independencies that are displayed explicitly in the graph.

Of course, this and other difficulties will not dissuade econometricians to use graphs; that would rake a scientific revolution of Kuhnian proportions. (see Still, awareness of these complexity issues should give inquisitive students the ammunition to hasten the revolution and equip econometrics with modern tools of causal analysis.

They eventually will.

September 11, 2016

An interesting math and causality-minded club

Filed under: Announcement — bryantc @ 6:08 pm

from Adam Kelleher:

The math and algorithm reading group ( is based in NYC, and was founded when I moved here three years ago. It’s a very casual group that grew out of a reading group I was in during graduate school. Some friends who were math graduate students were interested in learning more about general relativity, and I (a physicist) was interested in learning more math. Together, we read about differential geometry, with the goal of bringing our knowledge together. We reasoned that we could learn more as a group, by pooling our different perspectives and experience, than we could individually. That’s the core motivation of our reading group: not only are we there to help resolve each other get through the material if anyone gets stuck, but we’re also there to add what else we know (in the format of a group discussion) to the content of the material.

We’re currently reading Causality cover to cover. We’ve paused to implement some of the algorithms, and plan on pausing again soon for a review session. We intend to do a “hacking session”, to try our hands at causal inference and analysis on some open data sets.

Inspired by reading Causality, and realizing that the best open implementations of causal inference were packaged in the (old, relatively inaccessible) Tetrad package, I’ve started a modern implementation of some tools for causal inference and analysis in the causality package in Python. It’s on pypi (pip install causality, or check the tutorial on, but it’s still a work in progress. The IC* algorithm is implemented, along with a small suite of conditional independence tests. I’m adding some classic methods for causal inference and causal effects estimation, aimed at making the package more general-purpose. I invite new contributions to help build out the package. Just open an issue, and label it an “enhancement” to kick of the discussion!

Finally, to make all of the work more accessible to people without more advanced math background, I’ve been writing a series of blog posts aimed at introducing anyone with an intermediate background in probability and statistics to the material in Causality! It’s aimed especially at practitioners, like data scientists. The hope is that more people, managers included (the intended audience for the first 3 posts), will understand the issues that come up when you’re not thinking causally. I’d especially recommend the article about understanding bias, but the whole series (still in progress) is indexed here:

August 24, 2016

Simpson’s Paradox: The riddle that would not die. (Comments on four recent papers)

Filed under: Simpson's Paradox — bryantc @ 12:06 am

Contributor: Judea Pearl

If you search Google for “Simpson’s paradox,” as I did yesterday, you will get 111,000 results, more than any other statistical paradox that I could name. What elevates this innocent reversal of association to “paradoxical” status, and why it has captured the fascination of statisticians, mathematicians and philosophers for over a century are questions that we discussed at length on this (and other) blogs. The reason I am back to this topic is the publication of four recent papers that give us a panoramic view at how the understanding of causal reasoning has progressed in communities that do not usually participate in our discussions.

As readers of this blog recall, I have been trying since the publication of Causality (2000) to convince statisticians, philosophers and other scientific communities that Simpson’s paradox is: (1) a product of wrongly applied causal principles, and (2) that it can be fully resolved using modern tools of causal inference.

The four papers to be discussed do not fully agree with the proposed resolution.

To reiterate my position, Simpson’s paradox is (quoting Lord Russell) “another relic of a bygone age,” an age when we believed that every peculiarity in the data can be understood and resolved by statistical means. Ironically, Simpson’s paradox has actually become an educational tool for demonstrating the limits of statistical methods, and why causal, rather than statistical considerations are necessary to avoid paradoxical interpretations of data. For example, our recent book Causal Inference in Statistics: A Primer, uses Simpson’s paradox at the very beginning (Section 1.1), to show students the inevitability of causal thinking and the futility of trying to interpret data using statistical tools alone. See

Thus, my interest in the four recent articles stems primarily from curiosity to gauge the penetration of causal ideas into communities that were not intimately involved in the development of graphical or counterfactual models. Discussions of Simpson’s paradox provide a sensitive litmus test to measure the acceptance of modern causal thinking. “Talk to me about Simpson,” I often say to friendly colleagues, “and I will tell you how far you are on the causal trail.” (Unfriendly colleagues balk at the idea that there is a trail they might have missed.)

The four papers for discussion are the following:

Malinas, G. and Bigelow, J. “Simpson’s Paradox,” The Stanford Encyclopedia of Philosophy (Summer 2016 Edition), Edward N. Zalta (ed.), URL = <>.

Spanos, A., “Revisiting Simpson’s Paradox: a statistical misspecification perspective,” ResearchGate Article, <>, online May 2016.

Memetea, S. “Simpson’s Paradox in Epistemology and Decision Theory,” The University of British Columbia (Vancouver), Department of Philosophy, Ph.D. Thesis, May 2015.

Bandyopadhyay, P.S., Raghavan, R.V., Deruz, D.W., and Brittan, Jr., G. “Truths about Simpson’s Paradox Saving the Paradox from Falsity,” in Mohua Banerjee and Shankara Narayanan Krishna (Eds.), Logic and Its Applications, Proceedings of the 6th Indian Conference ICLA 2015 , LNCS 8923, Berlin Heidelberg: Springer-Verlag, pp. 58-73, 2015 .

——————- Discussion ——————-

1. Molina and Bigelow 2016 (MB)

I will start the discussion with Molina and Bigelow 2016 (MB) because the Stanford Encyclopedia of Philosophy enjoys both high visibility and an aura of authority. MB’s new entry is a welcome revision of their previous article (2004) on “Simpson’s Paradox,” which was written almost entirely from the perspective of “probabilistic causality,” echoing Reichenbach, Suppes, Cartwright, Good, Hesslow, Eells, to cite a few.

Whereas the previous version characterizes Simpson’s reversal as “A Logically Benign, empirically Treacherous Hydra,” the new version dwarfs the dangers of that Hydra and correctly states that Simpson’s paradox poses problem only for “philosophical programs that aim to eliminate or reduce causation to regularities and relations between probabilities.” Now, since the “probabilistic causality” program is fairly much abandoned in the past two decades, we can safely conclude that Simpson’s reversal poses no problem to us mortals. This is reassuring.

MB also acknowledge the role that graphical tools play in deciding whether one should base a decision on the aggregate population or on the partitioned subpopulations, and in testing one’s hypothesized model.

My only disagreement with the MB’s article is that it does not go all the way towards divorcing the discussion from the molds, notation and examples of the “probabilistic causation” era and, naturally, proclaim the paradox “resolved.” By shunning modern notation like do(x), Yx, or their equivalent, the article gives the impression that Bayesian conditionalization, as in P(y|x), is still adequate for discussing Simpson’s paradox, its ramifications and its resolution. It is not.

In particular, this notational orthodoxy makes the discussion of the Sure Thing Principle (STP) incomprehensible and obscures the reason why Simpson’s reversal does not constitute a counter example to STP. Specifically, it does not tell readers that causal independence is a necessary condition for the validity of the STP, (i.e., actions should not change the size of the subpopulations) and this independence is violated in the counterexample that Blyth contrived in 1972. (See

I will end with a humble recommendation to the editors of the Stanford Encyclopedia of Philosophy. Articles concerning causation should be written in a language that permits authors to distinguish causal from statistical dependence. I am sure future authors in this series would enjoy the freedom of saying “treatment does not change gender,” something they cannot say today, using Bayesian conditionalization. However, they will not do so on their own, unless you tell them (and their reviewers) explicitly that it is ok nowadays to deviate from the language of Reichenbach and Suppes and formally state: P(gender|do(treatment)) = P(gender).

Editorial guidance can play an incalculable role in the progress of science.

2. Comments on Spanos (2016)

In 1988, the British econometrician John Denis Sargan gave the following definition of an “economic model”: “A model is the specification of the probability distribution for a set of observations. A structure is the specification of the parameters of that distribution.” (Lectures on Advanced Econometric Theory (1988, p.27))

This definition, still cited in advanced econometric books (e.g., Cameron and Trivdi (2009) Microeconometrics) has served as a credo to a school of economics that has never elevated itself from the data-first paradigm of statistical thinking. Other prominent leaders of this school include Sir David Hendry, who wrote: “The joint density is the basis: SEMs (Structural Equation Models) are merely an interpretation of that.” Members of this school are unable to internalize the hard fact that statistics, however refined, cannot provide the information that economic models must encode to be of use to policy making. For them, a model is just a compact encoding of the density function underlying the data, so, two models encoding the same density function are deemed interchangeable.

Spanos article is a vivid example of how this statistics-minded culture copes with causal problems. Naturally, Spanos attributes the peculiarities of Simpson’s reversal to what he calls “statistical misspecification,” not to causal shortsightedness. “Causal” relationships do not exist in the models of Sargan’s school, so, if anything goes wrong, it must be “statistical misspecification,” what else? But what is this “statistical misspecification” that Spanos hopes would allow him to distinguish valid from invalid inference? I have read the paper several times, and for the life of me, it is beyond my ability to explain how the conditions that Spanos posits as necessary for “statistical adequacy” have anything to do with Simpson’s paradox. Specifically, I cannot see how “misspecified” data, which wrongly claims: “good for men, good for women, bad for people” suddenly becomes “well-specified” when we replace “gender” with “blood pressure”.

Spanos’ conditions for “statistical adequacy” are formulated in the context of the Linear Regression Model and invoke strictly statistical notions such as normality, linearity, independence etc. None of them applies to the binary case of {treatment, gender, outcome} in which Simpson’s paradox is usually cast. I therefore fail to see why replacing “gender” with “blood pressure” would turn an association from “spurious” to “trustworthy”.

Perhaps one of our readers can illuminate the rest of us how to interpret this new proposal. I am at a total loss.

For fairness, I should add that most economists that I know have second thoughts about Sargan’s definition, and claim to understand the distinction between structural and statistical models. This distinction, unfortunately, is still badly missing from econometric textbooks, see I am sure it will get there some day; Lady Science is forgiving, but what about economics students?

3. Memetea (2015)

Among the four papers under consideration, the one by Memetea, is by far the most advanced, comprehensive and forward thinking. As a thesis written in a philosophy department, Memetea treatise is unique in that it makes a serious and successful effort to break away from the cocoon of “probabilistic causality” and examines Simpson’s paradox to the light of modern causal inference, including graphical models, do-calculus, and counterfactual theories.

Memetea agrees with our view that the paradox is causal in nature, and that the tools of modern causal analysis are essential for its resolution. She disagrees however with my provocative claim that the paradox is “fully resolved”. The areas where she finds the resolution wanting are mediation cases in which the direct effect (DE) differs in sign from the total effect (TE). The classical example of such cases (Hesslow 1976) tells of a birth control pill that is suspected of producing thrombosis in women and, at the same time, has a negative indirect effect on thrombosis by reducing the rate of pregnancies (pregnancy is known to encourage thrombosis).

I have always argued that Hesslow’s example has nothing to do with Simpson’s paradox because it compares apples and oranges, namely, it compare direct vs. total effects where reversals are commonplace. In other words, Simpson’s reversal evokes no surprise in such cases. For example, I wrote, “we are not at all surprised when smallpox inoculation carries risks of fatal reaction, yet reduces overall mortality by irradicating smallpox. The direct effect (fatal reaction) in this case is negative for every subpopulation, yet the total effect (on mortality) is positive for the population as a whole.” (Quoted from When a conflict arises between the direct and total effects, the investigator need only decide what research question represents the practical aspects of the case in question and, once this is done, the appropriate graphical tools should be invoked to properly assess DE or TE. [Recall, complete algorithms are available for both, going beyond simple adjustment, and extending to other counterfactually defined effects (e.g., ETT, causes-of-effect, and more).]

Memetea is not satisfied with this answer. Her condition for resolving Simpson’s paradox requires that the analyst be told whether it is the direct or the total effect that should be the target of investigation. This would require, of course, that the model includes information about the investigator’s ultimate aims, whether alternative interventions are available (e.g. to prevent pregnancy), whether the study result will be used by a policy maker or a curious scientist, whether legal restrictions (e.g., on sex discrimination) apply to the direct or the total effect, and so on. In short, the entire spectrum of scientific and social knowledge should enter into the causal model before we can determine, in any given scenario, whether it is the direct or indirect effect that warrants our attention.

This is a rather tall order to satisfy given that our investigators are fairly good in determining what their research problem is. It should perhaps serve as a realizable goal for artificial intelligence researchers among us, who aim to build an automated scientist some day, capable of reasoning like our best investigators. I do not believe though that we need to wait for that day to declare Simpson’s paradox “resolved”. Alternatively, we can declare it resolved modulo the ability of investigators to define their research problems.

4. Comments on Bandyopadhyay, etal (2015)

There are several motivations behind the resistance to characterize Simpson’s paradox as a causal phenomenon. Some resist because causal relationships are not part of their scientific vocabulary, and some because they think they have discovered a more cogent explanation, which is perhaps easier to demonstrate or communicate.

Spanos’s article represents the first group, while Bandyopadhyay etal’s represents the second. They simulated Simpson’s reversal using urns and balls and argued that, since there are no interventions involved in this setting, merely judgment of conditional probabilities, the fact that people tend to make wrong judgments in this setting proves that Simpson’s surprise is rooted in arithmetic illusion, not in causal misinterpretation.

I have countered this argument in and I think it is appropriate to repeat the argument here.

“In explaining the surprise, we must first distinguish between ‘Simpson’s reversal’ and ‘Simpson’s paradox’; the former being an arithmetic phenomenon in the calculus of proportions, the latter a psychological phenomenon that evokes surprise and disbelief. A full understanding of Simpson’s paradox should explain why an innocent arithmetic reversal of an association, albeit uncommon, came to be regarded as `paradoxical,’ and why it has captured the fascination of statisticians, mathematicians and philosophers for over a century (though it was first labeled ‘paradox’ by Blyth (1972)) .

“The arithmetics of proportions has its share of peculiarities, no doubt, but these tend to become objects of curiosity once they have been demonstrated and explained away by examples. For instance, naive students of probability may expect the average of a product to equal the product of the averages but quickly learn to guard against such expectations, given a few counterexamples. Likewise, students expect an association measured in a mixture distribution to equal a weighted average of the individual associations. They are surprised, therefore, when ratios of sums, (a+b)/(c+d), are found to be ordered differently than individual ratios, a/c and b/d.1 Again, such arithmetic peculiarities are quickly accommodated by seasoned students as reminders against simplistic reasoning.

“In contrast, an arithmetic peculiarity becomes ‘paradoxical’ when it clashes with deeply held convictions that the peculiarity is impossible, and this occurs when one takes seriously the causal implications of Simpson’s reversal in decision-making contexts.  Reversals are indeed impossible whenever the third variable, say age or gender, stands for a pre-treatment covariate because, so the reasoning goes, no drug can be harmful to both males and females yet beneficial to the population as a whole. The universality of this intuition reflects a deeply held and valid conviction that such a drug is physically impossible.  Remarkably, such impossibility can be derived mathematically in the calculus of causation in the form of a ‘sure-thing’ theorem (Pearl, 2009, p. 181):

‘An action A that increases the probability of an event in each subpopulation (of C) must also increase the probability of B in the population as a whole, provided that the action does not change the distribution of the subpopulations.’2

“Thus, regardless of whether effect size is measured by the odds ratio or other comparisons, regardless of whether  is a confounder or not, and regardless of whether we have the correct causal structure on hand, our intuition should be offended by any effect reversal that appears to accompany the aggregation of data.

“I am not aware of another condition that rules out effect reversal with comparable assertiveness and generality, requiring only that Z not be affected by our action, a requirement satisfied by all treatment-independent covariates Z. Thus, it is hard, if not impossible, to explain the surprise part of Simpson’s reversal without postulating that human intuition is governed by causal calculus together with a persistent tendency to attribute causal interpretation to statistical associations.”

1. In Simpson’s paradox we witness the simultaneous orderings: (a1+b1)/(c1+d1)(a2+b2)/(c2+d2), (a1/c1)< (a2/c2), and (b1/d1)< (b2/d2)
2. The no-change provision is probabilistic; it permits the action to change the classification of individual units so long as the relative sizes of the subpopulations remain unaltered.

Final Remarks
I used to be extremely impatient with the slow pace in which causal ideas have been penetrating scientific communities that are not used to talk cause-and-effect. Recently, however, I re-read Thomas Kuhn’ classic The Structure of Schientific Revolution and I found there a quote that made me calm, content, even humorous and hopeful. Here it is:

—————- Kuhn —————-

“The transfer of allegiance from paradigm to paradigm is a conversion experience that cannot be forced. Lifelong resistance, particularly from those whose productive careers have committed them to an older tradition of normal science, is not a violation of scientific standards but an index to the nature of scientific research itself.”
p. 151

“Conversions will occur a few at a time until, after the last holdouts have died, the whole profession will again be practicing under a single, but now a different, paradigm.”
p. 152

We are now seeing the last holdouts.



July 24, 2016

External Validity and Extrapolations

Filed under: Generalizability,Selection Bias — bryantc @ 7:52 pm

Author: Judea Pearl

The July issue of the Proceedings of the National Academy of Sciences contains several articles on Causal Analysis in the age of Big Data, among them our (Bareinboim and Pearl’s) paper on data fusion and external validity. Several nuances of this problem were covered earlier on this blog under titles such as transportability, generalizability, extrapolation and selection-bias, see and

The PNAS paper has attracted the attention of the UCLA Newsroom which issued a press release with a very accessible description of the problem and its solution. You can find it here:

A few remarks:
I consider the mathematical solution of the external validity problem to be one of the real gems of modern causal analysis. The problem has its roots in the writings of 18th century demographers and its more recent awareness is usually associated with Campbell (1957) and Cook and Campbell (1979) writings on quasi-experiments. Our formal treatment of the problem using do-calculus has reduced it to a puzzle in logic and graph theory (see Bareinboim has further given this puzzle a complete algorithmic solution.

I said it is a gem because solving any problem instance gives me as much pleasure as solving a puzzle in ancient Greek geometry. It is in fact more fun than solving geometry problems, for two reasons.

First, when you stare at any external validity problem you do not have a clue whether it has or does not have a solution (i.e., whether an externally valid estimate exists or not) yet after a few steps of analysis — Eureka — the answer shines at you with clarity and says: “how could you have missed me?”. It is like communicating secretly with the oracle of Delphi, who whispers in your ears: “trisecting an angle?” forget it; “trisecting a line segment?” I will show you how. A miracle!

Second, while geometrical construction problems reside in the province of recreational mathematics, external validity is a serious matter; it has practical ramifications in every branch of science.

My invitation to readers of this blog: Anyone with intellectual curiosity and a thrill for mathematical discovery, please join us in the excitement over the mathematical solution of the external validity problem. Try it, and please send us your impressions.

It is hard for me to predict when scientists who critically need solutions to real-life extrapolation problems would come to recognize that an elegant and complete solution now exists for them. Most of these scientists (e.g., Campbell’s disciples) do not read graphs and cannot therefore heed my invitation. Locked in a graph-deprived vocabulary, they are left to struggle with meta-analytic techniques or opaque re-calibration routines (see waiting perhaps for a more appealing invitation to discover the availability of a solution to their problems.

It will be interesting to see how long it would take, in the age of internet.

July 9, 2016

The Three Layer Causal Hierarchy

Filed under: Causal Effect,Counterfactual,Discussion,structural equations — bryantc @ 8:57 pm

Recent discussions concerning causal mediation gave me the impression that many researchers in the field are not familiar with the ramifications of the Causal Hierarchy, as articulated in Chapter 1 of Causality (2000, 2009). This note presents the Causal Hierarchy in table form (Fig. 1) and discusses the distinctions between its three layers: 1. Association, 2. Intervention, 3. Counterfactuals.


June 28, 2016

On the Classification and Subsumption of Causal Models

Filed under: Causal Effect,Counterfactual,structural equations — bryantc @ 5:32 pm

From Christos Dimitrakakis:

>> To be honest, there is such a plethora of causal models, that it is not entirely clear what subsumes what, and which one is equivalent to what. Is there a simple taxonomy somewhere? I thought that influence diagrams were sufficient for all causal questions, for example, but one of Pearl’s papers asserts that this is not the case.

Reply from J. Pearl:

Dear Christos,

From my perspective, I do not see a plethora of causal models at all, so it is hard for me to answer your question in specific terms. What I do see is a symbiosis of all causal models in one framework, called Structural Causal Model (SCM) which unifies structural equations, potential outcomes, and graphical models. So, for me, the world appears simple, well organized, and smiling. Perhaps you can tell us what models lured your attention and caused you to see a plethora of models lacking subsumption taxonomy.

The taxonomy that has helped me immensely is the three-level hierarchy described in chapter 1 of my book Causality: 1. association, 2. intervention, and 3 counterfactuals. It is a useful hierarchy because it has an objective criterion for the classification: You cannot answer questions at level i unless you have assumptions from level i or higher.

As to influence diagrams, the relations between them and SCM is discussed in Section 11.6 of my book Causality (2009), Influence diagrams belong to the 2nd layer of the causal hierarchy, together with Causal Bayesian Networks. They lack however two facilities:

1. The ability to process counterfactuals.
2. The ability to handle novel actions.

To elaborate,

1. Counterfactual sentences (e.g., Given what I see, I should have acted differently) require functional models. Influence diagrams are built on conditional and interventional probabilities, that is, p(y|x) or p(y|do(x)). There is no interpretation of E(Y_x| x’) in this framework.

2. The probabilities that annotate links emanating from Action Nodes are interventional type, p(y|do(x)), that must be assessed judgmentally by the user. No facility is provided for deriving these probabilities from data together with the structure of the graph. Such a derivation is developed in chapter 3 of Causality, in the context of Causal Bayes Networks where every node can turn into an action node.

Using the causal hierarchy, the 1st Law of Counterfactuals and the unification provided by SCM, the space of causal models should shine in clarity and simplicity. Try it, and let us know of any questions remaining.


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