Causal Analysis in Theory and Practice

Dear friends in causality research,

This greeting is somewhat different from those you have been receiving in the past 18 years (Yes, it has been that long, see http://causality.cs.ucla.edu/blog, January 1, 2000). Instead of new results, passionate discussions, breakthroughs, controversies, and question and answers sessions, this greeting brings you a musical offering: The Book of Why. It is a new book that I have co-authored recently with Dana MacKenzie (http://danamackenzie.com), forthcoming May 15, 2018. The book tells the story, in layman’s terms, of the new science of cause and effect, the one we have been nourishing, playing with, and marveling at on this blog.

By “the new science” I mean going back, not merely to the causal revolution of the past few decades, but all the way to the day when scientists first assigned a mathematical symbol to a causal relation.

Joining me in this journey you will see how leaders in your own field managed to cope with the painful transition from statistical to causal thinking.

Despite my personal obsession with mathematical tools, this book has taught me that the story of causal inference looks totally different from the conceptual, non-technical viewpoint of our intended readers. So different in fact that I occasionally catch myself tuning to the music of The Book of Why when seeking a deeper understanding of a dry equation. I hope you and your students find it as useful and as enjoyable.

The publisher’s description can be viewed here: http://bayes.cs.ucla.edu/WHY/why-book-coming-soon.pdf while the Table of Content and sample chapters can be viewed here: http://bayes.cs.ucla.edu/WHY/

Our publisher also assures us that the book can be pre-ordered at no extra cost, and on your favorite website.

And may our story be inscribed in the book of worthy causes.

Judea

The following question was sent to us by Igor Mandel:

Separation of variables with zero causal coefficients from others
Here is a problem. Imagine, we have a researcher who has some understanding of the particular problem, and this understanding is partly or completely wrong. Can DAG or other (if any) causality theory convincingly establish this fact (that she is wrong)?

To be more specific, let’s consider a simple example with kind of undisputable causal variables (described in details in https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2984045 ). One wants to estimate, how different food’s ingredients affect the energy (in calories) containing in different types of food. She takes many samples and measures different things. But she doesn’t know about existence of the fats and proteins – yet she knows, that there are carbohydrates, water and fiber. She builds a respective DAG, how she feels it should be:

From our (i.e. educated people of 21st century) standpoint the arrows from Fiber and Water to Calories have zero coefficients. But since data bear significant correlations between Calories, Water and Fiber – any regression estimates would show non-zero values for these coefficients. Is there way to say, that these non-zero values are wrong, not just quantitatively, but kind of qualitatively?
Even brighter example of what is often called “spurious correlation”. It was “statistically proven” almost 20 years ago, that storks deliver babies ( http://robertmatthews.org/wp-content/uploads/2016/03/RM-storks-paper.pdf ) – while many women still believe they do not. How to reconvince those statistically ignorant women? Or – how to strengthen their naïve, but statistically not confirmed beliefs, just looking at the data and not asking them for some babies related details? What kind of DAG may help?

My Response
This question, in a variety of settings, has been asked by readers of this blog since the beginning of the Causal Revolution. The idea that new tools are now available that can handle causal problems free of statistical dogmas has encouraged thousands of researchers to ask: Can you do this, or can you do that? The answers to such questions are often trivial, and can be obtained directly from the logic of causal inference, without the details of the question. I am not surprised however that such questions surface again, in 2018, since the foundations of causal inference are rarely emphasized in the technical literature, so they tend to be forgotten.

I will answer Igor’s question as a student of modern logic of causation.

1. Can a DAG distinguish variables with zero causal effects (on Y) from those having non-zero effects.

Of course not, no method in the world can do that without further assumption. Here is why:
The question above concerns causal relations. We know from first principle that no causal query can be answered from data alone, without causal information that lies outside the data.
QED
[It does not matter if your query is quantitative or qualitative, if you address it to a story or to a graph. Every causal query needs causal assumptions. No causes in – no causes out (N. Cartwright)]

2. Can DAG-based methods do anything more than just quit with failure?

Of course they can.

2.1 First notice that the distinction between having or not having causal effect is a property of nature, (or the data generating process), not of the model that you postulate. We can therefore ignore the diagram that Igor describes above. Now, in addition to quitting for lack of information, DAG-based methods would tell you: “If you can give me some causal information, however qualitative, I will tell you if it is sufficient or not for answering your query.” I hope readers would agree with me that this kind of an answer, though weaker than the one expected by the naïve inquirer, is much more informative than just quitting in despair.

2.2 Note also that postulating a whimsical model like the one described by Igor above has no bearing on the answer. To do anything useful in causal inference we need to start with a model of reality, not with a model drawn by a confused researcher, for whom an arrow is nothing more than “data bears significant correlation” or “regression estimates show non-zero values.”

2.3 Once you start with a postulated model of reality, DAG-based methods can be very helpful. For example, they can take your postulated model and determine which of the arrows in the model should have a zero coefficient attached to it, which should have a non-zero coefficient attached to it, and which would remain undecided till the end of time.

2.4 Moreover, assume reality is governed by model M1 and you postulate model M2, different from M1. DAG-based methods can tell you which causal query you will answer correctly and which you will
answer incorrectly. (see section 4.3 of http://ftp.cs.ucla.edu/pub/stat_ser/r459-reprint-errata.pdf ). This is nice, because it offers us a kind of sensitivity analysis: how far should reality be from your assumed model before you will start making mistakes?

2.5 Finally, DAG-based methods identify for us the testable implication of our model, so that we can test models for compatibility with data.

I am glad Igor raised the question that he did. There is a tendency to forget fundamentals, and it is healthy to rehearse them periodically.

– Judea

Dear friends in causality research,

Welcome to the 2018 Winter Greeting from the UCLA Causality Blog. This greeting discusses the following topics:

1.  A report is posted, on the “What If” workshop at the NIPS conference  (see December 19, 2017 post below). It discusses my presentation of: Theoretical Impediments to Machine Learning, a newly revised version of which can be viewed here. [http://ftp.cs.ucla.edu/pub/stat_ser/r475.pdf]

2. New posting: “Facts and Fiction from the Missing Data Framework”. We are inviting discussion of two familiar mantras:
Mantra-1. “The role of missing data analysis in causal inference is well understood (eg causal inference theory based on counterfactuals relies on the missing data framework).
and
Mantra-2. “while missing data methods can form tools for causal inference, the converse cannot be true.”

We explain why we believe both mantras to be false, but we would like to hear you opinion before firming up our minds.

3. A review paper is available here:
http://ftp.cs.ucla.edu/pub/stat_ser/r473-L.pdf
Titled: “Graphical Models for Processing Missing Data.” It explains and demonstrates why missing data is a causal inference problem.

4. A new page is now up, providing information on “The Book of Why”
http://bayes.cs.ucla.edu/WHY/
It contains Table of Contents and excerpts from the book.

5. Nominations are now open for the ASA Causality in Education Award. The nomination deadline is March 1, 2018. For more information, please see
http://www.amstat.org/education/causalityprize/.

6. For those of us who were waiting patiently for the Korean translation of Primer — our long wait is finally over. The book is available now in colorful cover and in optimistic North Korean accent.
http://www.kyowoo.co.kr/02_sub/view.php?p_idx=1640&cate=0014_0017_

Don’t miss the gentlest introduction to causal inference.
http://bayes.cs.ucla.edu/PRIMER/

Enjoy, and have a productive 2018.
JP

Last month, Karthika Mohan and I received a strange review from a prominent Statistical Journal. Among other comments, we found the following two claims about a conception called “missing data framework.”

Claim-1: “The role of missing data analysis in causal inference is well understood (eg causal inference theory based on counterfactuals relies on the missing data framework).
and
Claim-2: “While missing data methods can form tools for causal inference, the converse cannot be true.”

I am sure that you have seen similar claims made in the literature, in lecture notes, in reviews of technical papers, or informal conversations in the cafeteria. Oddly, based on everything that we have read and researched about missing data we came to believe that both statements are false. Still, these claims are being touted widely, routinely, and  unabashedly, with only scattered attempts to explicate their content in open discussions.

Below, we venture to challenge the two claims, hoping to elicit your comments, and to come to some understanding of what actually is meant by the phrase “missing data framework;” what is being “framed” and what remains “un-framed.”

Challenging Claim-1

It is incorrect to suppose that the role of missing data analysis in causal inference is “well understood.” Quite the opposite. Researchers adhering to missing data analysis invariably invoke an ad-hoc assumption called “conditional ignorability,” often decorated as “ignorable treatment assignment mechanism”, which is far from being “well understood” by those who make it, let alone those who need to judge its plausibility.

For readers versed in graphical modeling, “conditional ignorability” is none other than the back-door criterion that students learn in the second class on causal inference, and which “missing-data” advocates have vowed to avoid at all cost. As we know, this criterion can easily be interpreted and verified when background knowledge is presented in graphical form but, as you can imagine, it turns into a frightening enigma for those who shun the light of graphs. Still, the simplicity of reading this criterion off a graph makes it easy to test whether those who rely heavily on ignorability assumptions know what they are assuming. The results of this test are discomforting.

Marshall Joffe, at John Hopkins University, summed up his frustration with the practice and “understanding” of ignorability in these words: “Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually, largely because they justify the use of available statistical methods and not because they are truly believed.” [Joffe, etal 2010, “Selective Ignorability Assumptions in Causal Inference,” The International Journal of Biostatistics: Vol. 6: Iss. 2, Article 11.  DOI: 10.2202/1557-4679.1199 Available at: http://www.bepress.com/ijb/vol6/iss2/11 ]

My personal conversations with leaders of the missing data approach to causation (these include seasoned researchers, educators and prolific authors) concluded with an even darker picture. None of those leaders was able to take a toy-example of 3-4 variables and determine whether conditional ignorability holds in the examples presented. It is not their fault, or course; determining
conditional ignorability is a hard cognitive and computational task that ordinary mortals cannot accomplish in their head, without the aids of graphs. (I base this assertion both on first-hand experience with students and colleagues and on intimate familiarity with issues of problem complexity and cognitive loads.)

Unfortunately, the mantra: “missing data analysis in causal inference is well understood” continues to be chanted at an ever increasing intensity, building faith among the faithful, and luring chanters to assume ignorability as self evident. Worse yet, the mantra blinds researchers from seeing how an improved level of understanding can emerge by abandoning the missing-data prism altogether, and conducting causal analysis in its natural habitat, using scientific models of reality rather than unruly patterns of missingness in the data.

A typical example of this trend is a recent article by Ding and Fan titled: “Causal Inference: A missing data perspective”.
https://arxiv.org/pdf/1712.06170.pdf
Sure enough, already on the ninth line of the abstract, the authors assume away non-ignorable treatments and, then, having  reached the safety zone of classical statistics, launch statistical estimation exercises on a variety of estimands. This creates the impression that “missing data perspective” is sufficient for  conducting “causal inference” when, in fact, the entire analysis rests on the assumption of ignorability, the one assumption that the missing data perspective lacks the tools to address.

The second part of Claim-1 is equally false: “causal inference theory based on counterfactuals relies on the missing data framework”. This may be true for the causal inference theory developed
by Rubin (1974) and expanded in Imbens and Rubin book (2015), but certainly not for the causal inference theory developed in (Pearl, 2000 2009) which is also based on counterfactuals, yet in no way relies on “the missing data framework”. On the contrary, page after page of (Pearl, 2000, 2009) emphasizes that counterfactuals are natural derivatives of the causal model used, and do not
require the artificial interpolation tools (eg imputations or matching) advocated by the missing data paradigm. Indeed, model-blind imputation can be shown to invite disasters in the class of “non ignorable” problems, something that is rarely acknowledged in the imputation-addicted literature. The very idea that certain parameters are not estimable, regardless of how clever the imputation is foreign to the missing data way of thinking. The same goes for the idea that some parameters are estimable while others are not.

In the past five years, we have done extensive reading into the missing data literature. [For a survey, see: http://ftp.cs.ucla.edu/pub/stat_ser/r473-L.pdf] It has become clear to us that this framework falls short of addressing three fundamental problems of modern causal analysis (1) To find if there exist sets of covariates that render treatments “ignorable”, (2) To estimate causal effects in cases where such sets do not exist, and (3) To decide if one’s modeling assumptions are compatible with the observed data.

It takes a theological leap of faith to imagine that a framework avoiding these fundamental problems can serve as an intellectual basis for a general theory of causal inference, a theory that has tackled those problems head on, and successfully so. Causal inference theory has advanced significantly beyond this stage – nonparametric estimability conditions have been established for causal and counterfactual relationships in both ignorable and non-ignorable problems. Can a framework bound to ignorability assumptions serve as a basis for one that has emancipated itself from such assumptions? We doubt it.

Challenging Claim 2.

We come now to claim (2), concerning the possibility of causality-free interpretation of missing data problems. It is possible indeed to pose a missing data problem in purely statistical terms, totally void of “missingness mechanism” vocabulary, void even of conditional independence assumptions. But this is rarely done, because the answer is trivial: none of the parameters of interest would be estimable without such assumptions (i.e, the likelihood function is flat). In theory, one can argue that there is really nothing causal about “missingness mechanism” as conceptualized by Rubin (1976), since it is defined in terms of conditional independence relations, a purely statistical notion that requires no reference to causation.

Not quite! The conditional independence relations that define missingness mechanisms are fundamentally different from those invoked in standard statistical analysis. In standard statistics, independence assumptions are presumed to hold in the distribution that governs the observed data, whereas in missing-data problems, the needed independencies are assumed to hold in the distribution of variables which are only partially observed. In other words, the independence assumptions invoked in missing data analysis are necessarily judgmental, and only rarely do they have
testable implications in the available data. [Fully developed in: http://ftp.cs.ucla.edu/pub/stat_ser/r473-L.pdf]

This behooves us to ask what kind of knowledge is needed for making reliable conditional independence judgments about a specific, yet partially observed problem domain. The graphical models literature has an unambiguous answer to this question: our judgment about statistical dependencies stems from our knowledge about causal dependencies, and the latters are organized in graphical form. The non-graphical literature has thus far avoided this question, presumably because it is a psychological issue that resides outside the scope of statistical analysis.

Psychology or not, the evidence from behavioral sciences is overwhelming that judgments about statistical dependence emanate from causal intuition. [see D. Kahneman “Thinking, Fast and Slow”
Chapter 16: Causes Trump Statistics]

In light of these considerations we would dare call for re-examination of the received mantra: 2.  “while missing data methods can form tools for causal inference, the converse cannot be true.” and reverse it, to read:

2′.  “while causal inference methods provide tools for solving missing data problems, the converse cannot be true.”

We base this claim on the following observations: 1. The assumptions needed to define the various types of missing data mechanisms are causal in nature. Articulating those assumption in causal vocabulary is natural, and results therefore in model transparency and credibility. 2. Estimability analysis based on causal modeling of missing data problems has charted new territories, including problems in the MNAR category (ie, Missing Not At Random), which were inaccessible to conventional missing-data analysis. In comparison, imputation-based approaches to missing data
do not provide guarantees of convergence (to consistent estimates) except for the narrow and unrecognizable class of problems in which ignorability holds. 3. Causal modeling of missing data problems has uncovered new ways of testing assumptions, which are infeasible in conventional missing-data analysis.

Perhaps even more convincingly, we were able to prove that no algorithm exists which decides if a parameter is estimable, without examining the causal structure of the model; statistical information is insufficient.

We hope these arguments convince even the staunchest missing data enthusiast to switch mantras and treat missing data problems for what they are: causal inference problems.

Judea Pearl, UCLA,
Karthika Mohan, UC Berkeley
———————————————–

Dear friends in causality research,

Welcome to the 2017 Mid-summer greeting from the Ucla Causality Blog.

This greeting discusses the following topics:

1. “The Eight Pillars of Causal Wisdom” and the WCE 2017 Virtual Conference Website.
2. A discussion panel: “Advances in Deep Neural Networks”,
3. Comments on “The Tale Wagged by the DAG”,
4. A new book: “The book of Why”,
5. A new paper: Disjunctive Counterfactuals,
6. Causality in Education Award,
7. News on “Causal Inference: A  Primer”

1. “The Eight Pillars of Causal Wisdom”

The tenth annual West Coast Experiments Conference was held at UCLA on April 24-25, 2017, preceded by a training workshop  on April 23.

You will be pleased to know that the WCE 2017 Virtual Conference Website is now available here:
http://spp.ucr.edu/wce2017/
It provides videos of the talks as well as some of the papers and presentations.

The conference brought together scholars and graduate students in economics, political science and other social sciences who share an interest in causal analysis. Speakers included:

1. Angus Deaton, on Understanding and misunderstanding randomized controlled trials.
2. Chris Auld, on the on-going confusion between regression vs. structural equations in the econometric literature.
3. Clark Glymour, on Explanatory Research vs Confirmatory Research.
4. Elias Barenboim, on the solution to the External Validity problem.
5. Adam Glynn, on Front-door approaches to causal inference.
6. Karthika Mohan, on Missing Data from a causal modeling perspective.
7. Judea Pearl, on “The Eight Pillars of Causal Wisdom.”
8. Adnan Darwiche, on Model-based vs. Model-Blind Approaches to Artificial Intelligence.
9. Niall Cardin, Causal inference for machine learning.
10. Karim Chalak, Measurement Error without Exclusion.
11. Ed Leamer, “Causality Complexities Example: Supply and Demand.
12. Rosa Matzkin, “Identification is simultaneous equation.
13 Rodrigo Pinto, Randomized Biased-controlled Trials.

The video of my lecture “The Eight Pillars of Causal Wisdom” can be watched here:
https://www.youtube.com/watch?v=8nHVUFqI0zk
A transcript of the talk can be found here:
http://spp.ucr.edu/wce2017/Papers/eight_pillars_of.pdf

2. “Advances in Deep Neural Networks”

As part of the its celebration of the 50 years of the Turing Award, the ACM has organized several discussion sessions on selected topics in computer science. I participated in a panel discussion on
“Advances in Deep Neural Networks”, which gave me an opportunity to share thoughts on whether learning methods based solely on data fitting can ever achieve a human-level intelligence. The discussion video can be viewed here:
https://www.youtube.com/watch?v=mFYM9j8bGtg
A position paper that defends these thoughts is available here:
web.cs.ucla.edu/~kaoru/theoretical-impediments.pdf

3. The Tale Wagged by the DAG

An article by this title, authored by Nancy Krieger and George Davey Smith has appeared in the International Journal of Epidemiology, IJE 2016 45(6) 1787-1808.
https://academic.oup.com/ije/issue/45/6#250304-2617148
It is part of a special IJE issue on causal analysis which, for the reasons outlined below, should be of interest to readers of this blog.

As the title tell-tales us, the authors are unhappy with the direction that modern epidemiology has taken, which is too wedded to a two-language framework:
(1) Graphical models (DAGs) — to express what we know, and
(2) Counterfactuals (or potential outcomes) — to express what we wish to know.

The specific reasons for the authors unhappiness are still puzzling to me, because the article does not demonstrate concrete alternatives to current methodologies. I can only speculate however that it is the dazzling speed with which epidemiology has modernized its tools that lies behind the authors discomfort. If so, it would be safe for us to assume that the discomfort will subside as soon as researchers gain greater familiarity with the capabilities and flexibility of these new tools.  I nevertheless recommend that the article, and the entire special issue of IJE be studied by our readers, because they reflect an interesting soul-searching attempt by a forward-looking discipline to assess its progress in the wake of a profound paradigm shift.

Epidemiology, as I have written on several occasions, has been a pioneer in accepting the DAG-counterfactuals symbiosis as a ruling paradigm — way ahead of mainstream statistics and its other satellites. (The social sciences, for example, are almost there, with the exception of the model-blind branch of econometrics. See Feb. 22 2017 posting)

In examining the specific limitations that Krieger and Davey Smith perceive in DAGs, readers will be amused to note that these limitations coincide precisely with the strengths for which DAGs are praised.

For example, the article complains that DAGs provide no information about variables that investigators chose not to include in the model.  In their words: “the DAG does not provide a comprehensive picture. For example, it does not include paternal factors, ethnicity, respiratory infections or socioeconomic position…” (taken from the Editorial introduction). I have never considered this to be a limitation of DAGs or of any other scientific modelling. Quite the contrary. It would be a disaster if models were permitted to provide information unintended by the modeller. Instead, I have learned to admire the ease with which DAGs enable researchers to incorporate knowledge about new variables, or new mechanisms, which the modeller wishes
to embrace.

Model misspecification, after all,  is a problem that plagues every  exercise in causal inference, no matter what framework one chooses to adapt. It can only be cured by careful model-building
strategies, and by enhancing the modeller’s knowledge. Yet, when it comes to minimizing misspecification errors, DAGS have no match. The transparency with which DAGs display the causal assumptions in the model, and the ease with which the DAG identifies the testable implications of those assumptions are incomparable; these facilitate speedy model diagnosis and repair with no match in sight.

Or, to take another example, the authors call repeatedly for an ostensibly unavailable methodology which they label “causal triangulation” (it appears 19 times in the article). In their words: “In our field, involving dynamic populations of people in dynamic societies and ecosystems, methodical triangulation of diverse types of evidence from diverse types of study settings and involving diverse populations is essential.”  Ironically, however, the task of treating “diverse type of evidence from diverse populations” has been accomplished quite successfully in the dag-counterfactual framework. See, for example the formal and complete results of (Bareinbaum and Pearl, 2016, http://ftp.cs.ucla.edu/pub/stat_ser/r450-reprint.pdf) which have emerged from DAG-based perspective and invoke the do-calculus. (See also  http://ftp.cs.ucla.edu/pub/stat_ser/r400.pdf) It is inconceivable for me to imagine anyone pooling data from two different designs (say
experimental and observational) without resorting to DAGs or (equivalently) potential outcomes, I am open to learn.

Another conceptual paradigm which the authors hope would liberate us from the tyranny of DAGs and counterfactuals is Lipton’s (2004) romantic aspiration for “Inference to the Best Explanation.” It is a compelling, century old mantra, going back at least to Charles Pierce theory of abduction (Pragmatism and Pragmaticism, 1870) which, unfortunately, has never operationalized its key terms: “explanation,” “Best” and “inference to”.  Again, I know of only one framework in which this aspiration has been explicated with sufficient precision to produce tangible results — it is the structural framework of DAGs and counterfactuals. See, for example, Causes of Effects and Effects of Causes”
http://ftp.cs.ucla.edu/pub/stat_ser/r431-reprint.pdf
and Halpern and Pearl (2005) “Causes and explanations: A structural-model approach”
http://ftp.cs.ucla.edu/pub/stat_ser/r266-part1.pdf

In summary, what Krieger and Davey Smith aspire to achieve by abandoning the structural framework has already been accomplished with the help and grace of that very framework.
More generally, what we learn from these examples is that the DAG-counterfactual symbiosis is far from being a narrow “ONE approach to causal inference” which ” may potentially lead to spurious causal inference” (their words). It is in fact a broad and flexible framework within which a plurality of tasks and aspirations can be formulated, analyzed and implemented. The quest for metaphysical alternatives is not warranted.

I was pleased to note that, by and large, commentators on Krieger and Davey Smith paper seemed to be aware of the powers and generality of the DAG-counterfactual framework, albeit not exactly for the reasons that I have described here. [footnote: I have many disagreements with the other commentators as well, but I wish to focus here on the TALE WAGGED DAG where the problems appear more glaring.] My talk on “The Eight Pillars of Causal Wisdom” provides a concise summary of those reasons and explains why I take the poetic liberty of calling these pillars “The Causal Revolution”
http://spp.ucr.edu/wce2017/Papers/eight_pillars_of.pdf

All in all, I believe that epidemiologists should be commended for the incredible progress they have made in the past two decades. They will no doubt continue to develop and benefit from the new tools that the DAG-counterfactual symbiosis has spawn. At the same time, I hope that the discomfort that Krieger and Davey Smith’s have expressed will be temporary and that it will inspire a greater understanding of the modern tools of causal inference.

Comments on this special issue of IJE are invited on this blog.

4. The Book of WHY

As some of you know, I am co-authoring another book, titled: “The Book of Why: The new science of cause and effect”. It will attempt to present the eight pillars of causal wisdom to the general public using words, intuition and examples to replace equations. My co-author is science writer Dana MacKenzie (danamackenzie.com) and our publishing house is Basic Books. If all goes well, the book will see your shelf by March 2018. Selected sections will appear periodically on this blog.

5. Disjunctive Counterfactuals

The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994) excludes  disjunctive conditionals, such as “had X been x1 or x2”, as well as disjunctive actions such as do(X=x1 or X=x2).  In contrast, the closest-world interpretation of Lewis ( 1973) assigns truth values to all counterfactual sentences, regardless of the logical form of the antecedant. The next issue of the Journal of Causal Inference will include a paper that extends the vocabulary of structural counterfactuals with disjunctions, and clarifies the assumptions needed for the extension. An advance copy can be viewed here:
http://ftp.cs.ucla.edu/pub/stat_ser/r459.pdf

6.  ASA Causality in Statistics Education Award

Congratulations go to Ilya Shpitser, Professor of Computer Science at Johns Hopkins University, who is the 2017 recipient of the ASA Causality in Statistics Education Award.  Funded by Microsoft Research and Google, the $5,000 Award, will be presented to Shpitser at the 2017 Joint Statistical Meetings (JSM 2017) in Baltimore.

Professor Shpitser has developed Masters level graduate course material that takes causal inference from the ivory towers of research to the level of students with a machine learning and data science background. It combines techniques of graphical and counterfactual models and provides both an accessible coverage of the field and excellent conceptual, computational and project-oriented exercises for students.

These winning materials and those of the previous Causality in Statistics Education Award winners are available to download online at http://www.amstat.org/education/causalityprize/

Information concerning nominations, criteria and previous winners can be viewed here:
http://www.amstat.org/ASA/Your-Career/Awards/Causality-in-Statistics-Education-Award.aspx
and here:
http://magazine.amstat.org/blog/2012/11/01/pearl/

7. News on “Causal Inference: A Primer”

Wiley, the publisher of our latest book “Causal Inference in Statistics: A Primer” (2016, Pearl, Glymour and Jewell) is informing us that the book is now in its 4th printing, corrected for all the errors we (and others) caught since the first publications. To buy a corrected copy, make sure you get the “4th “printing”. The trick is to look at the copyright page and make sure
the last line reads: 10 9 8 7 6 5 4

If you already have a copy, look up our errata page,
http://web.cs.ucla.edu/~kaoru/BIB5/pearl-etal-2016-primer-errata-pages-may2017.pdf
where all corrections are marked in red. The publisher also tells us the the Kindle version is much improved. I hope you concur.

Happy Summer-end, and may all your causes
produce healthy effects.
Judea

Dear friends in causality research,

We would like to promote an upcoming causality workshop at UAI 2017. See the details below for more information:

Causality in Learning, Inference, and Decision-making: Causality shapes how we view, understand, and react to the world around us. It’s a key ingredient in building AI systems that are autonomous and can act efficiently in complex and uncertain environments. It’s also important to the process of scientific discovery since it underpins how explanations are constructed and the scientific method.

Not surprisingly, the tasks of learning and reasoning with causal-effect relationships have attracted great interest in the artificial intelligence and machine learning communities. This effort has led to a very general theoretical and algorithmic understanding of what causality means and under what conditions it can be inferred. These results have started to percolate through more applied fields that generate the bulk of the data currently available, ranging from genetics to medicine, from psychology to economics.

This one-day workshop will explore causal inference in a broad sense through a set of invited talks,  open problems sessions, presentations, and a poster session. In this workshop, we will focus on the foundational side of causality on the one hand, and challenges presented by practical applications on the other. By and large, we welcome contributions from all areas relating to the study of causality.

We encourage co-submission of (full) papers that have been submitted to the main UAI 2017 conference. This workshop is a sequel to a successful predecessor at UAI 2016.

Dates/Locations: August 15, 2017; Sydney, Australia.

Speakers: TBA

Registration and additional information: https://www.cs.purdue.edu/homes/eb/causal-uai17/

Hello friends in causality research!

UCLA is proud to host the 2017 West Coast Experiments Conference. See the details below for more information:

West Coast Experiments Conference: The WCE is an annual conference that brings together leading scholars and graduate students in economics, political science and other social sciences who share an interest in causal identification broadly speaking. Now in its tenth year, the WCE is a venue for methodological instruction and debate over design-based and observational methods for causal inference, both theory and applications.

Speakers: Judea Pearl, Rosa Matzkin, Niall Cardin, Angus Deaton, Chris Auld, Jeff Wooldridge, Ed Leamer, Karim Chalak, Rodrigo Pinto, Clark Glymour, Elias Barenboim, Adam Glynn, and Karthika Mohan.

Dates/Location: The tenth annual West Coast Experiments Conference will be held at UCLA on Monday, April 24 and Tuesday, April 25, 2017, preceded by in-depth methods training workshops on Sunday, April 23. Events will be held in the Covel Commons Grand Horizon Ballroom, 200 De Neve Drive, Los Angeles, CA 90095.

Fees: Attendance is free!

Registration and details: Space is limited; for a detailed schedule of events and registration, please visit: wce2017ucla.eventbrite.com

Greetings!

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: http://statisticalhorizons.com/seminars/public-seminars/causal-inference-with-directed-graphs-spring

Greetings!

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: https://www.hsph.harvard.edu/causal/shortcourse/