Causal Analysis in Theory and Practice

Friends in causality research,
This Fall greeting from UCLA Causality blog contains:

A. News items concerning causality research,
B. New postings, new problems and new solutions.

A. News items concerning causality research
A1. The American Statistical Association has announced an early submission deadline for the 2015 “Causality in Statistics Education Award” — February 15, 2015.
For details and selection criteria, see http://www.amstat.org/education/causalityprize/

A2. Vol. 2 Issue 2 of the Journal of Causal Inference (JCI) is now out, and can be viewed here:
http://www.degruyter.com/view/j/jci.2014.2.issue-2/issue-files/jci.2014.2.issue-2.xml
As always, submissions are welcome on all aspects of causal analysis, especially those deemed methodological.

A3. New Tutorial: Causality for Policy Assessment and Impact Analysis, is offered by BayesiaLab , see here.

A4. A Conference on Counterfactual anaysis for Policy Evaluation will take place at USC, November 20, 2014
http://dornsife.usc.edu/conferences/cafe-conference-2014/

A5. A Conference focused on Causal Inference will take place at Kyoto, Japan, November 17-18, 2014
Kyoto International Conference on Modern Statistics in the 21st Century
General info: http://www.kakenhyoka.jp/conference/index_en.html
Program: http://www.kakenhyoka.jp/conference/file/program.pdf

B. New postings, new problems and new solutions.
B1. A confession of a graph-avoiding econometrician.

Guido Imbens explains why some economists do not find causal graphs to be helpful. Miquel Porta describes the impact of causal graphs in epidemiology as a “revolution”. The question naturally arises: “Are economists smarter than epidemiologists?” or, “What drives epidemiologists to seek the light of new tools while graph-avoiding economists resign to parial blindness?”

See [link] for attempted answer.

B2. Lord’s Paradox Revisited — (Oh Lord! Kumbaya!)

This is a historical journey which traces back Lord’s paradox from its original formulation (1967), resolves it using modern tools of causal analysis, explains why it presented difficulties in previous attempts at resolution and, finally, addresses the general issue of whether adjustments for pre-existing conditions is justified in group comparison applications.
Link: http://ftp.cs.ucla.edu/pub/stat_ser/r436.pdf

B3. “Causes of Effects and Effects of Causes”
http://ftp.cs.ucla.edu/pub/stat_ser/r431.pdf

An expansion of a previous note with same title, including additional demonstration that “causes of effects” are not metaphysical (Dawid, 2000) and a simple visualization of how the probability of necessity (PN) is shaped by experimental and observational findings. It comes together with “A note on Causes of Effects” link a rebuttal to recent attempts at mystification.

In a recent survey on Instrumental Variables (link), Guido Imbens fleshes out the reasons why some economists “have not felt that graphical models have much to offer them.”

His main point is: “In observational studies in social science, both these assumptions [exogeneity and exclusion] tend to be controversial. In this relatively simple setting [3-variable IV setting] I do not see the causal graphs as adding much to either the understanding of the problem, or to the analyses.” [page 377]

What Imbens leaves unclear is whether graph-avoiding economists limit themselves to “relatively simple settings” because, lacking graphs, they cannot handle more than 3 variables, or do they refrain from using graphs to prevent those “controversial assumptions” from becoming transparent, hence amenable to scientific discussion and resolution.

When students and readers ask me how I respond to people of Imbens’s persuasion who see no use in tools they vow to avoid, I direct them to the post “The deconstruction of paradoxes in epidemiology”, in which Miquel Porta describes the “revolution” that causal graphs have spawned in epidemiology. Porta observes: “I think the “revolution — or should we just call it a renewal”? — is deeply changing how epidemiological and clinical research is conceived, how causal inferences are made, and how we assess the validity and relevance of epidemiological findings.”

So, what is it about epidemiologists that drives them to seek the light of new tools, while economists (at least those in Imbens’s camp) seek comfort in partial blindness, while missing out on the causal revolution? Can economists do in their heads what epidemiologists observe in their graphs? Can they, for instance, identify the testable implications of their own assumptions? Can they decide whether the IV assumptions (i.e., exogeneity and exclusion) are satisfied in their own models of reality? Of course the can’t; such decisions are intractable to the graph-less mind. (I have challenged them repeatedly to these tasks, to the sound of a pin-drop silence)

Or, are problems in economics different from those in epidemiology? I have examined the structure of typical problems in the two fields, the number of variables involved, the types of data available, and the nature of the research questions. The problems are strikingly similar.

I have only one explanation for the difference: Culture.

The arrow-phobic culture started twenty years ago, when Imbens and Rubin (1995) decided that graphs “can easily lull the researcher into a false sense of confidence in the resulting causal conclusions,” and Paul Rosenbaum (1995) echoed with “No basis is given for believing” […] “that a certain mathematical operation, namely this wiping out of equations and fixing of variables, predicts a certain physical reality” [ See discussions here. ]

Lingering symptoms of this phobia are still stifling research in the 2nd decade of our century, yet are tolerated as scientific options. As Andrew Gelman put it last month: “I do think it is possible for a forward-looking statistician to do causal inference in the 21st century without understanding graphical models.” (link)

I believe the most insightful diagnosis of the phenomenon is given by Larry Wasserman:
“It is my impression that the “graph people” have studied the Rubin approach carefully while the reverse is not true.” (link)

A new review of my book *Causality* (Pearl, 2009) has appeared in the Journal of Structural Equation Modeling (SEM), authored by Stephen West and Tobias Koch (W-K). See http://bayes.cs.ucla.edu/BOOK-2K/west-koch-review2014.pdf

I find the main body of the review quite informative, and I thank the reviewers for taking the time to give SEM readers an accurate summary of each chapter, as well as a lucid description of the key ideas that tie the chapters together. However, when it comes to accepting the logical conclusions of the book, the reviewers seem reluctant, and tend to cling to traditions that lack the language, tools and unifying perspective to benefit from the chapters reviewed.

The reluctance culminates in the following paragraph:
“We value Pearl’s framework and his efforts to show that other frameworks can be translated into his approach. Nevertheless we believe that there is much to be gained by also considering the other major approaches to causal inference.”

W-K seem to value my “efforts” toward unification, but not the unification itself, and we are not told whether they doubt the validity of the unification, or whether they doubt its merits.
Or do they accept the merits and still see “much to be gained” by pre-unification traditions? If so, what is it that can be gained by those traditions and why can’t these gains be achieved within the unified framework presented in *Causality*?

To read more, click here.

Several readers had difficulty accessing my video interview with Professor Nicolas Jewell on “causality in statistics”.

I believe the following links should provide direct and smooth connection:
Part 1 of “Introduction to Causality” — interview with Nick Jewell, June 2014
Part 2 of “Introduction to Causality” — interview with Nick Jewell, June 2014

Coming up in September, BayesiaLab will conduct a conference and several courses at UCLA.

These include a 2-day Causal Inference Course (Sept. 19-20), a 3-Day introductory Bayesian Network Course (Sept. 16-18), and a BayesiaLab Users Conference (Sept. 23-24).

Details on program and registration can be obtained here:
http://www.bayesia.us/causal-inference-course
http://www.bayesia.us/2014-user-conference
Email: info@bayesia.us

Dear friends in causality research,

This greeting from UCLA Causality blog contains:
A. News items concerning causality research,
B. New postings, publications, slides and videos,
C. New scientific questions and some answers.

A. News items concerning causality research
A.1 The American Statistical Association has announced the 2014 winners of the “Causality in Statistics Education Award.” See http://www.amstat.org/newsroom/pressreleases/2014-CausalityinStatEdAward.pdf

Congratulations go to the honorees, Maya Peterson and Laura B. Balzer (UC Berkeley, biostatistics department), who will each receive a $5000 and a plaque at the 2014 Joint Statistical Meetings (JSM 2014) in Boston.

A.2 Vol. 2 Issue 2 of the Journal of Causal Inference (JCI) is scheduled to appear September, 2014. The TOC can be viewed here: http://degruyter.com/view/j/jci (click on READ CONTENT, under the cover picture)
As always, submissions are welcome on all aspects of causal analysis, especially those deemed heretical.

A.3 The 2014 World Congress on Epidemiology (IEA) will include a pre-conference program with two short courses dedicated to causal inference.
http://www.iea-course.org/index.php/pre-conference-course/program/program
IEA-2014, Anchorage , Alaska, August 16, 2014,

B. New postings, publications, slides and videos
B1. An interesting blog page dedicated to Sewall Wright’s 1921 paper “Correlation and causation” can be viewed here http://evaluatehelp.blogspot.com/2014/05/wright1st.html

It is intruiging to see how the first causal diagram came to the attention of the scientific community, in 1921. (It was immediately attacked, of course, by students of Karl Pearson.)

B.2 A video of my recent interview with professor Nick Jewell (UC Berkeley) concerning Causal Inference in Statistics, can now be watched by going to www.statisticsviews.com and clicking on the link next to the image.

B.3 A new review of Causality (Cambridge, 2009) has appeared in the Journal of Structural Equation Models, authored by Stephen West and Tobias Koch. See http://bayes.cs.ucla.edu/BOOK-2K/west-koch-review2014.pdf
My comments on this review will be posted here in a few days; stay tuned.

B.4 The paper “Trygve Haavelmo and the Emergence of Causal Calculus” is now available online on Econometric Theory, (10 June 2014), see here.

To the best of my knowledge, this is the first article on modern causal analysis that managed to penetrate the walls of mainstream econometric literature. Only time will tell whether this publication would help soften the enigmatic resistance of traditional economists to modern tools of causal analysis. Oddly, even those economists who have came to accept the structural reading of counterfactuals (e.g., Heckman and Pinto, 2013) still find it difficult to accept the second principle of causal inference: reading independencies from the model’s structure. See http://ftp.cs.ucla.edu/pub/stat_ser/r420.pdf

At any rate, the editors, Olav Bjerkholt and Peter Phillips, deserve a medal of courage for their heroic effort to create a dialogue between two civilizations.

B.5 To further facilitate this dialogue, Bryant Chen and I wrote a survey paper http://ftp.cs.ucla.edu/pub/stat_ser/r428.pdf which summarizes and illustrates the benefits of graphical tools in the context of linear models, where most economists feel secure and comfortable.

C. New scientific questions and some answers
There are new postings on our home page http://bayes.cs.ucla.edu/csl_papers.html that might earn your attention. Among them:

R-425 “Recovering from Selection Bias in Causal and Statistical Inference,” with E. Bareinboim and J. Tian,
We ask: Is there a general, non-parametric solution to the selection-bias problem posed by Berkson
and Heckman decades ago?
The answer is: Yes. The problem is illuminated, generalized and solved using graphical models — the language where knowledge resides.
(The article just received the Best Paper Award at the Annual Conference of the American Association for Artificial Intelligence (AAAI-2014), July 30, 2014.)
http://ftp.cs.ucla.edu/pub/stat_ser/r425.pdf

R-431. “Causes of effects and Effects of Causes”.
Question: Is it really the case that modern methods of causal analysis have neglected to deal with “causes of effects”, as claimed by a recent paper of Dawid, Fienberg and Faigman (2013)?.
Answer: Quite the contrary! See here:
http://ftp.cs.ucla.edu/pub/stat_ser/r431.pdf

R-428. “Testable Implications of Linear Structural Equation Models” with Bryant Chen and Jin Tian.
We ask: Is there a systematic way of unveiling the testable implications of a linear model with latent variables?
Answer: We provide an algorithm for doing so.
http://ftp.cs.ucla.edu/pub/stat_ser/r428.pdf

Finally, dont miss previous postings on our blog, for example:
1. On Simpson’s Paradox. Again?
2. Who Needs Causal Mediation?
3. On model-based vs. ad-hoc methods

Wishing you a productive summer,
Judea

Simpson’s paradox must have an unbounded longevity, partly because traditional statisticians, so it seems, are still refusing to accept the fact that the paradox is causal, not statistical (link to R-414).

This was demonstrated recently in an April discussion on Gelman’s blog where the paradox was portrayed again as one of those typical cases where conditional associations are different from marginal associations. Strangely, only one or two discussants dared call: “Wait a minute! This is not what the paradox is about!” — to little avail.

To watch the discussion more closely, click http://andrewgelman.com/2014/04/08/understanding-simpsons-paradox-using-graph/ .

A recent discussion that might be of interest to readers took place on SEMNET, a Structural Equation Modeling Discussion Group, which appeals primarily to traditional SEM researchers who, generally speaking, are somewhat bewildered by the recent fuss about modern causal analysis. This particular discussion focused on “causal mediation”.

1.
An SEMNET user, Emil Coman, asked (my paraphrasing):
“Who needs causal mediation (CM)?”
All it gives us is: (a) the ability to cope with confounders of the M—>Y relation and (b) the ability to handle interactions. Both (a) and (b) are SEM-fixable; (a) by adjusting for those confounders and (b) by using Bengt Muthen’s software (Mplus), whenever we suspect interactions.

To continue, click here.

A lively discussion flared up early this month on Andrew Gelman’s blog (garnering 114 comments!) which should be of some interest to readers of this blog.

The discussion started by a quote from George Box (1979) on the advantages of model-based approaches, and drifted into related topics such as

(1) What is a model-based approach,

(2) Whether mainstream statistics encourages this approach,

(3) Whether statistics textbooks and education have given face to reality,

(4) Whether a practicing statistician should invest time learning causal modeling,

or wait till it “proves itself” in the real messy world?

I share highlights of this discussion here, because I believe many readers have faced similar disputations and misunderstandings in conversations with pre-causal statisticians.

To read more, click here.

We have received the following announcement from Isabelle Guyon, regarding the second contest on “cause-effect pairs”:

We launched a second edition of the cause-effect pairs challenge:

The ChaLearn Fast Causation Coefficient Challenge (ending June 15, 2014)
Link: https://www.codalab.org/competitions/1381

There are prizes and the winners will present their results at the Microsoft Faculty Summit in July 2014.