Dear colleagues in causality research,<\/p>\n
This is a Winter Greeting from the UCLA Causality blog, welcoming you back to a discussion on causality-related issues.<\/p>\n
This message contains
\n 1. Topics under discussion
\n 2. New results
\n 3. Information on journals, lectures, conferences, and software.<\/p>\n
1. Topics under Discussion 1.1 Principal Stratification – A goal or a tool?’<\/strong> Was posted for discussion in the International Journal of Biostatistics (IJB) in March 2011, and has elicited response from eight discussants on whether studies based on Principal Stratification estimate quantities that researchers care about.<\/p>\n I am about to wrap up the discussion with a summary-rejoinder, so, if you have comments or insights that where not brought up, feel free to communicate them to the IJB’s Editor, “Nicholas P. Jewell” 1.2 Comments and Controversies:<\/strong> A response to L&S is posted on our causality blog, proving them wrong, and questioning the wisdom of asking researchers to translate assumptions from a language where they stand out vividly and meaningfully into an Arrow-Phobic language where they can no longer be recognized, let alone justified. We have all the reason to suspect that L&S will come back.<\/p>\n 1.3 The Match-Maker Paradox<\/strong> 1.4 An On-going Causal-Inference Discussions on SEMNET (Structural Equation Modeling Discussion Group<\/strong> Topics include: Additionally, a weekly session is being conducted by Les Hayduk, going page by page over the R-370 chapter (http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r370.pdf<\/a>) and explaining it to novices in the field. It answers, I hope, all questions that rank and file researchers find perplexing when introduced to causal analysis.<\/p>\n 1.5 Draft Chapter on Causality and SEM<\/strong> 1.6 A Survey Paper on Adjustment<\/strong> The paper teaches researchers how to glance at a graph and determine when\/if an adjustment for one variable modifies the relationship between two other variables. It is a simple exercise for graphical modellers but extremely difficult one for economists and other researchers who ask such questions routinely and have no graphs for guidance.<\/p>\n 1.7 A New Introduction to Causal Calculus<\/strong> My response, together with thoughts on the psychology of Simpson’s Paradox is below http:\/\/www.michaelnielsen.org\/ddi\/guest-post-judea-pearl-on-correlation-causation-and-the-psychology-of-simpsons-paradox\/<\/a><\/p>\n 1.8 Haavelmo and the Emergence of Causal Calculus<\/strong> Presented at Haavelmo Centennial Symposium, in Oslo, last December, the paper describes the cultural barriers that Haavelmo’s ideas have had to overcome in the past six decades and points to the fact that modern economists are still unaware of the benefits that Haavelmo’s ideas bestow upon them.<\/p>\n 2. New Results in Causal Inference<\/strong><\/p>\n 2.1 Interpretable Conditions for Identifying Natural Direct Effects.<\/strong> The paper lists four conditions that are sufficient for the identification of natural direct and indirect effects. The conditions do not invoke “ignorability” jargon thus permitting more informed judgment of the plausibility of the assumptions. It also shows that conditions usually cited in the literature are overly restrictive, and can be relaxed without compromising identification.<\/p>\n 2.2 Some Thoughts on Transfer Learning, with Applications to Meta-analysis and Data-sharing Estimation<\/strong> Summary: How to combine data from multiple and diverse environments so as to take full advantages of that which they share in common.<\/p>\n 2.3 Understanding Bias Amplification<\/strong> This note sheds a new light on the phenomenon of “bias amplification” by considering the cumulative effect of conditioning on multiple “near instruments,” and shows that bias amplification may build up at a faster rate than bias reduction.<\/p>\n 2.4 Local Characterizations of Causal Bayesian Networks<\/strong> The standard definition of Causal Bayesian Networks (CBN) requires that every interventional distribution be decomposable into a truncated product, dictated by the graph. This paper replaces this “global” definition with three alternative ones, each invoking “local” aspects of conditioning and intervening.<\/p>\n 3. Journals, Courses, Lectures and Conferences<\/strong> 3.2 The Journal of Causal Inference<\/strong> The first issue is planned for Summer of 2012 and, needless to state, you are invited to submit your latest results, and to bring JCI to the attention of students and colleagues who might be seeking a forum for presenting their latest ideas, results and, yes, breakthroughs!<\/p>\n The Journal of Causal Inference will highlight both the uniqueness and interdisciplinary nature of causal research. and will publish both theoretical and applied research including survey and discussion papers.<\/p>\n 3.3 Spring Workshop Graphical Causal Models<\/strong> This workshop will introduce graphical causal models, show how to simulate data from, and estimate such models in Tetrad, explain model search, and more…. 3.4 Conference: EVIDENCE AND CAUSALITY IN THE SCIENCE ECitS 2012<\/strong> 3.5 New Software tool for Causal Inference<\/strong> This paper announces the release of DAGitty, a graphical user interface for drawing and analyzing causal diagrams. DAGitty, offers several improvements over Kyono’s “COMMENTATOR” http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r364.pdf<\/a> among them efficient listing of all minimal sufficient adjustment sets. It is available under an open-source license obtained at www.dagitty.net and http:\/\/www.dagitty.net\/manual.pdf<\/a><\/p>\n Best wishes,<\/p>\n =======Judea<\/p>\n","protected":false},"excerpt":{"rendered":" Dear colleagues in causality research, This is a Winter Greeting from the UCLA Causality blog, welcoming you back to a discussion on causality-related issues. This message contains 1. Topics under discussion 2. New results 3. Information on journals, lectures, conferences, and software. 1. Topics under Discussion 1.1 Principal Stratification – A goal or a tool?’ […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,16],"tags":[],"class_list":["post-408","post","type-post","status-publish","format-standard","hentry","category-announcement","category-general"],"_links":{"self":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/408","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=408"}],"version-history":[{"count":0,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/408\/revisions"}],"wp:attachment":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=408"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=408"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n<\/strong><\/p>\n
\n http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r382.pdf<\/a>
\n http:\/\/escholarship.org\/uc\/item\/4xj9d380#page-3<\/a><\/p>\n
\nWe are being warned again that graphical models can produce “incorrect” causal inferences. The warning comes again from Lindquist and Sobel (LS), entitled “Cloak and DAG”: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811911013085<\/a><\/p>\n
\nAn apparent paradox concerning the representation of matching designs in DAGs was posted by Pablo Lardelli and resolved by noting that matching involves unit-to-unit interaction and results in “persistent-unfaithfulness.”<\/p>\n
\nIn the past four months I have spent time discussing modern approaches to causal inference with SEM researchers who, by and large, are still practicing the traditional methods associated with the acronym “SEM”. The discussions are fully documented and archived on http:\/\/bama.ua.edu\/archives\/semnet.html<\/a><\/p>\n
\n a. the causal\/statistical distinction.
\n b. the structural-regressional distinction
\n c. The residual\/disturbance distinction
\n d. The assumptions conveyed by each structural equation.
\n e. The counterfactual reading of structural equations
\n f. What the Mediation Formula tells us about mediation and policy questions.
\n g. Mediators and Moderators.
\n h. d-separation, equivalent models and the testable implications of structural models
\n i. The logic of SEM as an inference engine.<\/p>\n
\nKen Bollen and I finished a draft chapter titled “Eight Myths about Causality and Structural Equation Models.” It covers the history of misconceptions about SEM, including recent assaults by the Arrow-Phobic Society.
\n see http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r393.pdf<\/a><\/p>\n
\nGreenland, S., and Pearl J., “Adjustments and their Consequences — Collapsibility Analysis using Graphical Models” http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r369.pdf<\/a><\/p>\n
\nAn excellent introduction to causal diagrams and do-calculus was posted recently by bloggist-author Michael Nielsen, titled “If correlation doesn’t imply causation, then what does?” It can be accessed here: http:\/\/www.michaelnielsen.org\/ddi\/if-correlation-doesnt-imply-causation-then-what-does\/<\/a><\/p>\n
\nhttp:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r391.pdf<\/a><\/p>\n
\nhttp:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r389.pdf<\/a><\/p>\n
\nhttp:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r387.pdf<\/a><\/p>\n
\nhttp:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r386.pdf<\/a><\/p>\n
\nby Bareinboim, Brito and Pearl http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r384.pdf<\/a><\/p>\n
\n 3.1 Tutorial: Causal Inference in Statistics
\n<\/strong>
\nI will be giving a tutorial (IOL) on causal inference at the upcoming JSM 2012 conference in San Diego California, July 29th, 4-5:50 PM. If any of your students or colleagues wishes to attend, the abstract and further details can be found on http:\/\/www.amstat.org\/meetings\/jsm\/2012\/onlineprogram\/AbstractDetails.cfm?abstractid=304318<\/a><\/p>\n
\nAs reported in the last blog email, the Journal of Causal Inference (JCI) was launched on September 2011 and the website is open for submissions. http:\/\/www.bepress.com\/jci<\/a><\/p>\n
\nFriday 3\/30\/2012
\nNorthwestern University, Chicago<\/p>\n
\nLecturers: Richard Scheines and Joseph Ramsey CMU
\nFor details see chicagochapterasa@gmail.com
\nhttp:\/\/community.amstat.org\/Chicago_Chapter\/Calendar\/20112012\/NewItem6\/<\/a><\/p>\n
\nCentre for Reasoning, University of Kent, 5-7 September 2012
\nOrganizers: Phyllis Illari and Federica Russo
\nhttp:\/\/www.kent.ac.uk\/secl\/philosophy\/jw\/2012\/ecits\/<\/a><\/p>\n
\nDAGitty: A Graphical Tool for Analyzing Causal Diagrams by Textor, Johannes; Hardt, Juliane; Kn|ppel, Sven
\n Epidemiology: September 2011 – Volume 22 – Issue 5 – p 745 doi: 10.1097\/EDE.0b013e318225c2be<\/p>\n