Judea Pearl and colleagues have launched the Journal of Causal Inference, to be published by the Berkeley Electronic Press.
Tuesday, August 30, 2011
Sunday, August 7, 2011
Causal-Inference References from SEMNET
by Alan
Over at the Structural Equation Modeling discussion listserve (SEMNET), participants lately have recommended several recent articles and resources on causal inference (and related topics) with non-experimental (correlational) research designs. For benefit of the larger research community, I have listed these materials below.
I've looked over some of these articles and they seem to vary in the prior training assumed. Some would seem accessible for social scientists without elaborate mathematical training, whereas others refer extensively to more sophisticated math (e.g., matrix algebra). The Antonakis et al. piece, in particular, appears to provide a (mostly) non-technical overview.
Three specific topics are covered in many of the articles:
Omitted-variable bias (or specification error, more generally), which is all-important to causal inference, due to the "third-variable" issue.
Propensity-score modeling, which already has been discussed extensively on this blog (e.g., here, here, and here).
The use of instrumental variables.
I am particularly impressed by the range of academic discliplines from which these articles arise. Thanks to those who contributed these items to SEMNET!
----------------------------------------------------------------------------------
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086-1120.
Austin, P.C. (2011). A tutorial and case study in propensity score analysis: An application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate Behavioral Research, 46, 119-151.
Beguin, J., Pothier, D., & Côté, S.D. (2011). Deer browsing and soil disturbance induce cascading effects on plant communities: a multilevel path analysis. Ecological Applications, 21, 439–451.
Bollen, K.A., Kirby, J.B., Curran, P.J., Paxton, P.M., & Chen, F. (2007). Latent variable models under misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) estimators. Sociological Methods & Research, 36, 48-86.
Bollen, K.A., & Bauer, D.J. (2004). Automating the selection of model-implied instrumental variables. Sociological Methods & Research, 32, 425-452.
Bollen, K.A., & Maydeu-Olivares, A. (2007). A polychoric instrumental variable (PIV) estimator for structural equation models with categorical variables. Psychometrika, 72, 309-326.
Clarke, K. (2005). The Phantom Menace: Omitted variable bias in econometric research. Conflict Management and Peace Science, 22, 341-352.
Clarke, K. (2009). Return of the Phantom Menace: Omitted variable bias in econometric research. Conflict Management and Peace Science, 26, 46-66.
Coffman, D.L. (2011). Estimating causal effects in mediation analysis using propensity scores. Structural Equation Modeling, 18, 357-369.
Freedman, D.A., Collier, D., Sekhon, J.S., & Stark, P.B. (Eds.). (2009). Statistical models and causal inference: A dialogue with the social sciences. Cambridge University Press. ISBN: 978-0521123909.
Frosch, C.A., & Johnson-Laird, P.N. (2011). Is everyday causation deterministic or probabilistic? Acta Psychologica, 137, 280-291.
Hancock, G. R., & Harring, J. R. (2011, May). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Paper presented at the Modern Modeling Methods conference, Storrs, CT. (Hancock webpage to request copy.)
Hoshino, T. (2008). A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm. Computational Statistics & Data Analysis, 52, 1413-1429.
Kirby, J.B., & Bollen, K.A. (2009). Using instrumental variable (IV) tests to evaluate model specification in latent variable structural equation models. Sociological Methodology, 39, 327–355. (Public copy)
Mahoney, J. (2008). Toward a unified theory of causality. Comparative Political Studies, 41, 412-436.
Markus, K.A. (2011). Mulaik on atomism, contraposition and causation. Quality and Quantity. Online First (subscription needed), http://www.springerlink.com/content/r754405614228w0v/
Markus, K.A. (2011). Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological resarch methods. In P. McKay Illari, F. Russo & J. Williamson (Eds.),Causality in the sciences (pp. 240-269). Oxford, UK: Oxford University Press. (Errata)
Pearl, J. (2010). On a class of bias-amplifying variables that endanger effect estimates. Technical Report R-356. In P. Grunwald & P. Spirtes (Eds.), Proceedings of UAI, 417-424. Corvallis, OR: AUAI.
Pearl, J. (2011, August). The causal foundations of structural equation modeling. UCLA Cognitive Systems Laboratory, Technical Report (R-370), http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf. Chapter for R. H. Hoyle (Ed.), Handbook of structural equation modeling. New York: Guilford Press.
Shadish, W.R., & Steiner, P.M. (2010). A primer on propensity score analysis. Newborn & Infant Nursing Review, 10, 19-26.
Shipley, B. (2009). Confirmatory path analysis in a generalized multilevel context. Ecology, 90, 363-368.
Shipley, B. The Causal Toolbox: A collection of programs for testing or exploring causal relationships [website]. http://pages.usherbrooke.ca/jshipley/recherche/book.htm
Spector, P.E., & Brannick, M.T. (2011). Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods, 14, 287-305.
Steiner, P.M., Cook, T.D., Shadish, W.R., & Clark, M.H. (2010). The importance of covariate selection in controlling for selection bias in observational studies. Psychological Methods, 15, 250-267.
Thoemmes, F.J., & Kim, E.S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46, 90-118.
Over at the Structural Equation Modeling discussion listserve (SEMNET), participants lately have recommended several recent articles and resources on causal inference (and related topics) with non-experimental (correlational) research designs. For benefit of the larger research community, I have listed these materials below.
I've looked over some of these articles and they seem to vary in the prior training assumed. Some would seem accessible for social scientists without elaborate mathematical training, whereas others refer extensively to more sophisticated math (e.g., matrix algebra). The Antonakis et al. piece, in particular, appears to provide a (mostly) non-technical overview.
Three specific topics are covered in many of the articles:
I am particularly impressed by the range of academic discliplines from which these articles arise. Thanks to those who contributed these items to SEMNET!
----------------------------------------------------------------------------------
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086-1120.
Austin, P.C. (2011). A tutorial and case study in propensity score analysis: An application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate Behavioral Research, 46, 119-151.
Beguin, J., Pothier, D., & Côté, S.D. (2011). Deer browsing and soil disturbance induce cascading effects on plant communities: a multilevel path analysis. Ecological Applications, 21, 439–451.
Bollen, K.A., Kirby, J.B., Curran, P.J., Paxton, P.M., & Chen, F. (2007). Latent variable models under misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) estimators. Sociological Methods & Research, 36, 48-86.
Bollen, K.A., & Bauer, D.J. (2004). Automating the selection of model-implied instrumental variables. Sociological Methods & Research, 32, 425-452.
Bollen, K.A., & Maydeu-Olivares, A. (2007). A polychoric instrumental variable (PIV) estimator for structural equation models with categorical variables. Psychometrika, 72, 309-326.
Clarke, K. (2005). The Phantom Menace: Omitted variable bias in econometric research. Conflict Management and Peace Science, 22, 341-352.
Clarke, K. (2009). Return of the Phantom Menace: Omitted variable bias in econometric research. Conflict Management and Peace Science, 26, 46-66.
Coffman, D.L. (2011). Estimating causal effects in mediation analysis using propensity scores. Structural Equation Modeling, 18, 357-369.
Freedman, D.A., Collier, D., Sekhon, J.S., & Stark, P.B. (Eds.). (2009). Statistical models and causal inference: A dialogue with the social sciences. Cambridge University Press. ISBN: 978-0521123909.
Frosch, C.A., & Johnson-Laird, P.N. (2011). Is everyday causation deterministic or probabilistic? Acta Psychologica, 137, 280-291.
Hancock, G. R., & Harring, J. R. (2011, May). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Paper presented at the Modern Modeling Methods conference, Storrs, CT. (Hancock webpage to request copy.)
Hoshino, T. (2008). A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm. Computational Statistics & Data Analysis, 52, 1413-1429.
Kirby, J.B., & Bollen, K.A. (2009). Using instrumental variable (IV) tests to evaluate model specification in latent variable structural equation models. Sociological Methodology, 39, 327–355. (Public copy)
Mahoney, J. (2008). Toward a unified theory of causality. Comparative Political Studies, 41, 412-436.
Markus, K.A. (2011). Mulaik on atomism, contraposition and causation. Quality and Quantity. Online First (subscription needed), http://www.springerlink.com/content/r754405614228w0v/
Markus, K.A. (2011). Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological resarch methods. In P. McKay Illari, F. Russo & J. Williamson (Eds.),Causality in the sciences (pp. 240-269). Oxford, UK: Oxford University Press. (Errata)
Pearl, J. (2010). On a class of bias-amplifying variables that endanger effect estimates. Technical Report R-356. In P. Grunwald & P. Spirtes (Eds.), Proceedings of UAI, 417-424. Corvallis, OR: AUAI.
Pearl, J. (2011, August). The causal foundations of structural equation modeling. UCLA Cognitive Systems Laboratory, Technical Report (R-370), http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf. Chapter for R. H. Hoyle (Ed.), Handbook of structural equation modeling. New York: Guilford Press.
Shadish, W.R., & Steiner, P.M. (2010). A primer on propensity score analysis. Newborn & Infant Nursing Review, 10, 19-26.
Shipley, B. (2009). Confirmatory path analysis in a generalized multilevel context. Ecology, 90, 363-368.
Shipley, B. The Causal Toolbox: A collection of programs for testing or exploring causal relationships [website]. http://pages.usherbrooke.ca/jshipley/recherche/book.htm
Spector, P.E., & Brannick, M.T. (2011). Methodological urban legends: The misuse of statistical control variables. Organizational Research Methods, 14, 287-305.
Steiner, P.M., Cook, T.D., Shadish, W.R., & Clark, M.H. (2010). The importance of covariate selection in controlling for selection bias in observational studies. Psychological Methods, 15, 250-267.
Thoemmes, F.J., & Kim, E.S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46, 90-118.
Tuesday, February 1, 2011
Sunday, December 19, 2010
Correlation, Causality, and Parenting Studies
Alan and Bo are both quoted in this new article from Brain, Child magazine. In addition to discussing substantive issues of causal inference, author Katy Read also probes the chain of diffusion of social-science research.
The chain begins, of course, with the investigators who conducted the research. Those who conduct correlational studies typically include a statement of limitations at the end of their articles, noting that the findings are open to alternative causal interpretations. In less-guarded moments, however, even research scientists will use phraseology that implies a preferred causal direction.
Universities, research institutes, and/or professional organizations may then issue press releases on a particular study. Ultimately, a research finding may make it into the media. At each reporting step removed from the (methodologically trained) scientific investigators, therefore, statements of caution regarding causality are less likely to appear.
The chain begins, of course, with the investigators who conducted the research. Those who conduct correlational studies typically include a statement of limitations at the end of their articles, noting that the findings are open to alternative causal interpretations. In less-guarded moments, however, even research scientists will use phraseology that implies a preferred causal direction.
Universities, research institutes, and/or professional organizations may then issue press releases on a particular study. Ultimately, a research finding may make it into the media. At each reporting step removed from the (methodologically trained) scientific investigators, therefore, statements of caution regarding causality are less likely to appear.
Saturday, November 27, 2010
Studying Personality as a Causal Agent
by Alan
Angela Lee Duckworth, Eli Tsukayama, and Henry May have published an article entitled, "Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control" (abstract) in the October 2010 issue of the new journal Social Psychological and Personality Science.
The authors are interested in the personality trait of self-control (which encompasses such abilities as persistence and delay of gratification) and whether it can actually be shown to cause academic achievement (GPA) during the years from fifth to eighth grade. Duckworth and colleagues acknowledge the difficulty early on:
"Manipulating personality in a random-assignment experiment could, in theory, establish its causal role for later outcomes but, alas, personality is not easily manipulated. To our knowledge, no empirical investigation to date has successfully manipulated trait-level self-control and measured subsequent effects on life outcomes" (p. 311).
The authors also carefully review the arguments for why longitudinal predictive models using path-analysis and structural equation modeling (controlling for prior levels of the dependent variable), though an improvement over cross-sectional correlations, are still vulnerable to unmeasured third variables. "Longitudinal growth-curve modeling using hierarchical linear models (HLM)" is then presented as offering "a partial solution to the third variable problem" (p. 312).
The core of the argument appears to be this: "By treating a predictor as a time-varying covariate in the prediction of trajectories, one can rule out the possibility of all time invariant confounds (e.g., relatively stable variables such as socioeconomic status). Specifically, if short-term changes in a predictor predict subsequent short-term changes in achievement, a confounding variable z would have to predict these changes and also be tightly yoked to changes in the predictor over time (i.e., the confound and predictor would have to go up and down together in synchrony over time)" (p. 312).
The authors indeed found self-control to predict GPA longitudinally (and not the reverse), concluding as follows:
"This longitudinal HLM study illustrated an innovative analytic strategy that effectively controlled for all time-invariant third-variable confounds... What our analyses did not rule out, however, is the possibility of an unmeasured time-varying third variable that changes in sync with self-control and causally determines subsequent academic performance" (p. 316; my emphasis added).
I would recommend this article for its excellent exposition on causality with non-experimental designs and for the approach it demonstrates. There are a lot of technical details in the article, as well, and even readers experienced with some of the general techniques used may find themselves having to pause periodically to wrap their minds around each specific analytic decision the authors describe.
Angela Lee Duckworth, Eli Tsukayama, and Henry May have published an article entitled, "Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control" (abstract) in the October 2010 issue of the new journal Social Psychological and Personality Science.
The authors are interested in the personality trait of self-control (which encompasses such abilities as persistence and delay of gratification) and whether it can actually be shown to cause academic achievement (GPA) during the years from fifth to eighth grade. Duckworth and colleagues acknowledge the difficulty early on:
"Manipulating personality in a random-assignment experiment could, in theory, establish its causal role for later outcomes but, alas, personality is not easily manipulated. To our knowledge, no empirical investigation to date has successfully manipulated trait-level self-control and measured subsequent effects on life outcomes" (p. 311).
The authors also carefully review the arguments for why longitudinal predictive models using path-analysis and structural equation modeling (controlling for prior levels of the dependent variable), though an improvement over cross-sectional correlations, are still vulnerable to unmeasured third variables. "Longitudinal growth-curve modeling using hierarchical linear models (HLM)" is then presented as offering "a partial solution to the third variable problem" (p. 312).
The core of the argument appears to be this: "By treating a predictor as a time-varying covariate in the prediction of trajectories, one can rule out the possibility of all time invariant confounds (e.g., relatively stable variables such as socioeconomic status). Specifically, if short-term changes in a predictor predict subsequent short-term changes in achievement, a confounding variable z would have to predict these changes and also be tightly yoked to changes in the predictor over time (i.e., the confound and predictor would have to go up and down together in synchrony over time)" (p. 312).
The authors indeed found self-control to predict GPA longitudinally (and not the reverse), concluding as follows:
"This longitudinal HLM study illustrated an innovative analytic strategy that effectively controlled for all time-invariant third-variable confounds... What our analyses did not rule out, however, is the possibility of an unmeasured time-varying third variable that changes in sync with self-control and causally determines subsequent academic performance" (p. 316; my emphasis added).
I would recommend this article for its excellent exposition on causality with non-experimental designs and for the approach it demonstrates. There are a lot of technical details in the article, as well, and even readers experienced with some of the general techniques used may find themselves having to pause periodically to wrap their minds around each specific analytic decision the authors describe.
Tuesday, September 28, 2010
Psychological Methods Article: Harder et al.
by Alan
Nearly two years ago, we first mentioned propensity scores as a tool for trying to draw as strong a causal inference as possible from nonexperimental designs, between exposure to a "treatment" experience and some later outcome. For example, how might attending private (vs. public) schools affect students' intellectual curiosity? An article by Harder, Stuart, and Anthony in the September 2010 Psychological Methods offers practical advice and concrete examples for conducting propensity-score analyses. Propensity scores involve matching the treatment and control groups on other variables (covariates) that are thought to predict treatment status or have "potential to confound the relationship between the treatment and the outcome" (p. 235). Harder et al. discuss several specific methods for implementing such matching, such as 1:1, 1:k (where one treated participant can be matched to multiple comparison persons), and full matching (where one matched set can include multiple treated individuals and multiple comparison counterparts). The article also discusses software packages for implementing propensity scores, although these do not appear very plentiful at the moment. The article is definitely worth a look by anyone contemplating a correlational or quasi-experimental study that one hopes to frame in causal terms (acknowledging that a full causal claim will be out of reach with such designs).
Nearly two years ago, we first mentioned propensity scores as a tool for trying to draw as strong a causal inference as possible from nonexperimental designs, between exposure to a "treatment" experience and some later outcome. For example, how might attending private (vs. public) schools affect students' intellectual curiosity? An article by Harder, Stuart, and Anthony in the September 2010 Psychological Methods offers practical advice and concrete examples for conducting propensity-score analyses. Propensity scores involve matching the treatment and control groups on other variables (covariates) that are thought to predict treatment status or have "potential to confound the relationship between the treatment and the outcome" (p. 235). Harder et al. discuss several specific methods for implementing such matching, such as 1:1, 1:k (where one treated participant can be matched to multiple comparison persons), and full matching (where one matched set can include multiple treated individuals and multiple comparison counterparts). The article also discusses software packages for implementing propensity scores, although these do not appear very plentiful at the moment. The article is definitely worth a look by anyone contemplating a correlational or quasi-experimental study that one hopes to frame in causal terms (acknowledging that a full causal claim will be out of reach with such designs).
Friday, June 4, 2010
Periodic Message from Judea Pearl (June 2010)
Judea Pearl recently released a message on behalf of the UCLA Causality Blog for interested scholars. It is reprinted below (with light editing, e.g., to make it easier to follow links):
Dear friends in causality,
Below are a few items you might find to be of some interest and possibly some challenge.
1. A new book containing a collection of recent articles on causation, some tutorial in nature, is now available from College Publications (2010).
Title: Heuristics, Probability and Causality
Editors: R. Dechter, H. Geffner and J. Halpern
For table of contents, preface and more information please click here. As you can see, I have had a natural indirect effect on the cover design, but zero controlled direct effect.
2. A symposium on causality and related topics by some of the contributors to Heuristics, Probabilities and Causality was held at UCLA on March 12. Videos of lectures, by C. Hitchcock, S. Greenland, T. Richardson, J. Robins, R. Scheines, J. Tian, Y. Shoham and J. Pearl, can be viewed here. Videos of additional lectures will be posted in the near future.
3. Recent entries on our Causality Blog include:
3.1
An open letter from Judea Pearl to Nancy Cartwright concerning "Causal Pluralism," a topic central to a discussion of her book Hunting Causes, which appeared recently in Economics and Philosophy 26:69-77.(Posted May 31, 2010)
3.2
A lively discussion by T. Richardson, J. Robins and J. Pearl on the structure of the causal hierarchy and the scientific role of untestable counterfactual assumptions.
(Posted May 3 and May 15, 2010)
4. A recent posting on my web-page is a paper titled, "The Mediation Formula: A guide to the assessment of causal pathways in non-linear models," which explains why traditional methods of mediation analysis yield distorted results when applied to discrete data, even when correct parametric models are assumed and all parameters are known precisely. The Mediation Formula circumvents these difficulties.
5. Another posting of potential interest is Technical Report R-364, by T. Kyono (Master Thesis), titled "Commentator: A Front-End User-Interface Module for Graphical and Structural Equation Modeling." It takes a DAG as input and prints: (1) all identifiable direct effects, (2) all identifiable causal effects, (3) all (minimal) sets of admissible covariates, (4) all instrumental variables, and (5) (almost) all testable implications of a model. The source code is available upon request.
6. Finally, I have received inquiries regarding a slide that I used at NYU, in which an instrumental variable poses as an innocent confounder and, upon adjustment, amplifies, rather than reduces confounding bias. The moral of the story was (and is) that "outcome assignment" is safer to model than "treatment assignment." The pertinent paper is R-356 (link).
7. As always, your thoughts are welcome and will surely be put into some good cause when conveyed to other blog readers.
Best,
Judea Pearl
UCLA
Dear friends in causality,
Below are a few items you might find to be of some interest and possibly some challenge.
1. A new book containing a collection of recent articles on causation, some tutorial in nature, is now available from College Publications (2010).
Title: Heuristics, Probability and Causality
Editors: R. Dechter, H. Geffner and J. Halpern
For table of contents, preface and more information please click here. As you can see, I have had a natural indirect effect on the cover design, but zero controlled direct effect.
2. A symposium on causality and related topics by some of the contributors to Heuristics, Probabilities and Causality was held at UCLA on March 12. Videos of lectures, by C. Hitchcock, S. Greenland, T. Richardson, J. Robins, R. Scheines, J. Tian, Y. Shoham and J. Pearl, can be viewed here. Videos of additional lectures will be posted in the near future.
3. Recent entries on our Causality Blog include:
3.1
An open letter from Judea Pearl to Nancy Cartwright concerning "Causal Pluralism," a topic central to a discussion of her book Hunting Causes, which appeared recently in Economics and Philosophy 26:69-77.(Posted May 31, 2010)
3.2
A lively discussion by T. Richardson, J. Robins and J. Pearl on the structure of the causal hierarchy and the scientific role of untestable counterfactual assumptions.
(Posted May 3 and May 15, 2010)
4. A recent posting on my web-page is a paper titled, "The Mediation Formula: A guide to the assessment of causal pathways in non-linear models," which explains why traditional methods of mediation analysis yield distorted results when applied to discrete data, even when correct parametric models are assumed and all parameters are known precisely. The Mediation Formula circumvents these difficulties.
5. Another posting of potential interest is Technical Report R-364, by T. Kyono (Master Thesis), titled "Commentator: A Front-End User-Interface Module for Graphical and Structural Equation Modeling." It takes a DAG as input and prints: (1) all identifiable direct effects, (2) all identifiable causal effects, (3) all (minimal) sets of admissible covariates, (4) all instrumental variables, and (5) (almost) all testable implications of a model. The source code is available upon request.
6. Finally, I have received inquiries regarding a slide that I used at NYU, in which an instrumental variable poses as an innocent confounder and, upon adjustment, amplifies, rather than reduces confounding bias. The moral of the story was (and is) that "outcome assignment" is safer to model than "treatment assignment." The pertinent paper is R-356 (link).
7. As always, your thoughts are welcome and will surely be put into some good cause when conveyed to other blog readers.
Best,
Judea Pearl
UCLA
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