Causal Analysis in Theory and Practice

October 26, 2013

Comments on Kenny’s Summary of Causal Mediation

Filed under: Counterfactual,Indirect effects,Mediated Effects — moderator @ 12:00 am

David Kenny’s website <> has recently been revised to include a section on the Causal Inference Approach to Mediation. As many readers know, Kenny has pioneered mediation analysis in the social sciences through his seminal papers with Judd (1981) and Baron(1986) and has been an active leader in this field. His original approach, often referred to as the “Baron and Kenny (BK) approach,” is grounded in conservative Structural Equation Modeling (SEM) analysis, in which causal relationships are asserted with extreme caution and the boundaries between statistical and causal notions vary appreciably among researchers.

It is very significant therefore that Kenny has decided to introduce causal mediation analysis to the community of SEM researchers which, until very recently, felt alienated from recent advances in causal mediation analysis, primarily due to the counterfactual vocabulary in which it was developed and introduced. With Kenny’s kind permission, I am posting his description below, because it is one of the few attempts to explain causal inference in the language of traditional SEM mediation analysis and, thus, it may serve to bridge the barriers between the two communities.

Next you can find Kenny’s new posting, annotated with my comments. In these comments, I have attempted to further clarify the bridges between the two cultures; the “traditional” and the “causal.” I will refer to the former as “BK” (for Baron and Kenny) and to the latter as “causal” (for lack of a better word) although, conceptually, both BK and SEM are fundamentally causal.

Click here for the full post.

October 8, 2013

UCLA-Stats seminar: A Conversation on Statistical Methodology, with Judea Pearl

Filed under: Announcement,Discussion,General — eb @ 11:50 pm

UCLA Department of Statistics Seminar Series

Thu, 10/24/2013, 12:30 PM—1:30 PM
4660 Geology Bldg.

Judea Pearl and Joakim Ekstrom

A Conversation on Statistical Methodology, with Judea Pearl

Join us for a conversation on statistical methodology, and in particular the theory of causal inference. In this ‘Socratic dialogue’-styled conversation, recent A.M. Turing award winner Judea Pearl will discuss his views on statistical methodology with conversational partner Joakim Ekstrom. The conversation will start at R.A. Fisher’s randomization procedure for isolation of contributors to systematic variation, and then continue discussing the methodology of Judea Pearl for isolation and identification of causal factors in data obtained from sources other than perfectly randomized experiments.

In the conversation, there will be plenty of opportunity for attendees to ask questions, explore alternatives and raise objections, especially regarding ways of introducing causal inference in statistics education.

Judea Pearl is a Professor at UCLA Computer Science and Statistics, and has contributed greatly to the theory of causal inference. Joakim Ekstrom is a post-doctoral research scholar at UCLA Statistics, seminar co-organizer, and an expert on the theory and history of statistics.

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