Causal Inference

22 Mar 2013 16:36

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Spun off from Causality. Graphical causal models are, I think very strongly, the best way to approach this, and so they get their own notebook.

Things I need to learn more about: non-linear and non-parametric instrumental variables estimators.

See also: Computational Mechanics; Graphical Models; Machine Learning, Statistical Inference, and Induction

Recommended (current big picture):
• Clark Glymour
  • The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology [Mini-review]
  • "What Went Wrong? Reflections on Science by Observation and The Bell Curve", Philosophy of Science 65 (1998): 1--32 [PDF reprint via Prof. Glymour]
• Sander Greenland, Judea Pearl and James M. Robins, "Causal Diagrams for Epidemiologic Research", Epidemiology 10 (1999): 37--48 [PDF via Prof. Pearl. Very much not just for epidemiologists.]
• Stephen L. Morgan and Christopher Winship, Counterfactuals and Causal Inference: Methods and Principles for Social Research [Mini-review]
• Judea Pearl
  • "Causal Inference in Statistics: An Overview", Statistics Surveys 3 (2009): 96--146
  • Causality: Models, Reasoning and Inference
• Donald B. Rubin and Richard P. Waterman, "Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology", math.ST/0609201 = Statistical Science 21 (2006): 206--222 [A good description of Rubin et al.'s methods for causal inference, adapted to the meanest understanding. I list this here rather than under "more specialized" because Rubin and Waterman do a very good job of explaining, in a clear and concrete problem, just how and why the newer techniques of causal inference are valuable, with just enough technical detail that it doesn't seem like magic. Rubin's paper-collection, Matched Sampling for Causal Effects, has much, much more if this appeals to you, though it is just a paper collection and not a proper book, so there's a lot of redundancy.]
• Peter Spirtes, Clark Glymour and Richard Scheines, Causation, Prediction and Search [Comments]

Recommended (more specialized):
• Kevin Arceneaux, Alan S. Gerber, Donald P. Green, "A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark", Sociological Methods and Research 39 (2010): 256--282 ["Cautionary" is not really strong enough.]
• Bryant Chen and Judea Pearl, "Regression and Causation: A Critical Examination of Econometrics Textbooks" [PDF preprint via Prof. Pearl]
• Tianjiao Chu and Clark Glymour, "Search for Additive Nonlinear Time Series Causal Models", Journal of Machine Learning Research 9 (2008): 967--991
• Diego Colombo, Marloes H. Maathuis, Markus Kalisch, Thomas S. Richardson, "Learning high-dimensional directed acyclic graphs with latent and selection variables", arxiv:1104.5617
• Angus Deaton, "Instruments, Randomization, and Learning about Development", Journal of Economic Literature 48 (2010): 424--455 [PDF reprint via Prof. Deaton]
• Vanessa Didelez, Sha Meng, Nuala A. Sheehan, "Assumptions of IV Methods for Observational Epidemiology", Statistical Science 25 (2010): 22--40, arxiv:1011.0595
• Felix Elwert and Nicholas A. Christakis, "Wives and Ex-Wives: A New Test for Homogamy Bias in the Widowhood Effect", Demography 45 (2008): 851--873 [PDF preprint courtesy of Prof. Elwert]
• Clive Granger, "Investigating Causal Relations by Econometric Models and Cross Spectral Methods", Econometrica 37 (1969): 424--439 [His original paper on what has come to be called "Granger causality". It's actually very interesting — I hadn't realized he got the idea from reading Norbert Wiener, but in retrospect that makes sense and explains why he formulated his test in the frequency domain — but I feel it's very much a dead end for actual causal inference.]
• Samantha Kleinberg, An Algorithmic Enquiry Concerning Causality [Ph.D. thesis, NYU, 2010; PDF]
• Gustavo Lacerda, Peter Spirtes, Joseph Ramsey and Patrik O. Hoyer, "Discovering Cyclic Causal Models by using Independent Components Analysis" [PDF draft via Gustavo]
• Marloes H. Maathuis, Markus Kalisch, Peter Bühlmann, "Estimating high-dimensional intervention effects from observational data", Annals of Statistics 37 (2009): 3133--31654, arxiv:0810.4214
• Milan Palus and Aneta Stefanovska, "Direction of coupling from phases of interacting oscillators: An information-theoretic approach", Physical Review E 67 (2003): 055201 [Thanks to Prof. Palus for a reprint. This is a kind of information-theoretic generalization of Granger causality.]
• Judea Pearl, "On a Class of Bias-Amplifying Covariates that Endanger Effect Estimates", Technical Report R-356, UCLA Cognitive Systems Lab, 2009 [Those would be instrumental variables (among others).]
• Tom Pepinsky, "OMFG Exogenous Variation! Or, Can You Find Good Nails When You Find an Indonesian Politics Hammer?" [Admittedly, less formal in presentation than many of the rest of these links]
• J. D. Ramsey, S. J. Hanson, C. Hanson, Y. O. Halchenko, R. A. Poldrack and C. Glymour, "Six Problems for Causal Inference from fMRI", NeuroImage 49 (2010): 1545--1558 [PDF via Prof. Hanson; thanks to Prof. Glymour for having shared a preprint with me]
• James M. Robins, Richard Scheines, Peter Spirtes and Larry Wasserman, "Uniform Consistency in Causal Inference", Biometrika 90 (2003): 491--515 [CMU Statistics Tech Report 725, 2000]
• Mark R. Rosenzweig and Kenneth I. Wolpin, "Natural "Natural Experiments" in Economics", Journal of Economic Literature 38 (2000): 827--874
• Heather Sarsons, "Rainfall and Conflict" [From the Annals of Invalid Instruments... PDF preprint]
• Herbert Simon
  • "Causal Ordering and Identifiability", in Studies in Econometric Method, 1953; reprinted as chapter 1 in Simon's Models of Man [PDF of the 1950 preprint version, as "The Causal Principle and the Identification Problem"]
  • "Spurious Correlation: A Causal Interpretation", Journal of the American Statistical Association 49 (1954): 467-479 [PDF reprint]
• Peter Spirtes, "Limits on Causal Inference from Observational Data" [PostScript preprint]
• Bastian Steudel and Nihat Ay, "Information-theoretic inference of common ancestors", arxiv:1010.5720
• Halbert White and Karim Chalak, "Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning", Journal of Machine Learning Research 10 (2009): 1759--1799 [Thanks to Doug White for a preprint]
• Christopher Winship
  • Counterfactual Causal Analysis [Repository page with papers aimed at sociological applications]
  • and Stephen L. Morgan, "Estimation of Causal Effects from Observational Data," Annual Review of Sociology 25 (1999): 659--706 [PDF reprint, large]
  • and Michael Sobel, "Causal Inference in Sociological Studies" [PDF preprint]

Recommended (historical):
• Hubert M. Blalock, Causal Inferences in Nonexperimental Research [Comments]
• Jerzy Neyman, "On the Application of Probability Theory to Agricultural Experiments: Essay on Principles, Section 9", Statistical Science 5 (1990): 465--472 [Translation of a portion of Neyman's 1923 dissertation]

Modesty forbids me to recommend:
• CRS, Advanced Data Analysis from an Elementary Point of View, Part III (chapters on causal inference for statistics students)
• CRS and Andrew C. Thomas, "Homophily and Contagion Are Generically Confounded in Observational Social Network Studies", arxiv:1004.4704 [Less-technical weblog version]

To read:
• Mickel Aickin, Causal Analysis in Biomedicine and Epidemiology: Based on Minimal Sufficient Causation
• Nicola Ancona, Daniele Marinazzo and Sebastiano Stramaglia, "Extending Granger causality to nonlinear systems", physics/0405009
• Aron Barbey and Phillip Wolff, "Learning Causal Structure from Reasoning", phil-sci/3176
• Michael Baumgartner, "Inferring Causal Complexity", phil-sci/2879 [Identifying causal structures among Boolean variables, handling "both mutually dependent causes, i.e. causal chains, and multiple effects, i.e. epiphenomena"]
• Alexandre Belloni, Victor Chernozhukov, Christian Hansen, "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls", arxiv:1201.0224
• Andrew Bennett, "Process Tracing and Causal Inference", phil-sci/8872
• Aaron P. Blaisdell, Kosuke Sawa, Kenneth J. Leising, and Michael R. Waldmann, "Causal Reasoning in Rats", Science 311 (2006): 1020--1022
• Hans-Peter Blossfeld and Gotz Rohwer, Techniques of Event-History Modeling: New Approach to Causal Analysis
• Zhihong Cai, Manabu Kuroki, "On Identifying Total Effects in the Presence of Latent Variables and Selection Bias", UAI 2008, arxiv:1206.3239
• Xiaohong Chen, Markus Reiss, "On rate optimality for ill-posed inverse problems in econometrics", arxiv:0709.2003 [Non-parametric instrumental variables]
• Yonghong Chen, Steven L. Bressler, and Mingzhou Ding, "Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data", q-bio.NC/0608034 = Journal of Neuroscience Methods 150 (2006): 228--237
• Timothy G. Conley, Christian B. Hansen and Peter E. Rossi, "Plausibly Exogenous", The Review of Economics and Statistics 94 (2012): 260--272
• Daniel Commenges, Anne Gegout-Petit, "A general dynamical statistical model with possible causal interpretation", Journal of the Royal Statistical Society B 71 (2009): 719--736, arxiv:0710.4396
• P. Daniusis, D. Janzing, J. Mooij, J. Zscheischler, B. Steudel, K. Zhang and B. Schölkopf, "Inferring deterministic causal relations", UAI 2010 [Abstract, preprint. I heard the talk, which was very interesting, but want to understand the idea better. If you fed this a seauence from the Arnold cat map, could it get the arrow of time?]
• A. Philip Dawid and Vanessa Didelez, "Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview", Statistics Surveys 4 (2010): 184--231
• Vanessa Didelez, Svend Kreiner and Niels Keiding, "Graphical Models for Inference Under Outcome-Dependent Sampling", Statistical Science 25 (2010): 368--387, arxiv:1101.0901
• Mingzhou Ding, Yonghong Chen and Steve L. Bressler, "Granger Causality: Basic Theory and Application to Neuroscience", q-bio.QM/0608035 = pp. 451--474 in B. Schelter, M. Winterhalder, and J. Timmer (eds.), Handbook of Time Series Analysis
• Patrick Doreian, "Causality in Social Network Analysis", Sociological Methods and Research 30 (2001): 81--114
• Thad Dunning, "Improving Causal Inference: Strengths and Limitations of Natural Experiments", Political Research Quarterly 61 (2008): 282--293 [PDF reprint via Prof. Dunning]
• Frederick Eberhardt and Richard Scheines, "Interventions and Causal Inference", phil-sci/2944
• Michael Eichler
  • "Graphical modelling of multivariate time series", math.ST/0610654
  • "Graphical Gaussian modelling of multivariate time series with latent variables", Journal of Machine Learning Research Proceedings 9 (2010): 193--200
• Elena Erosheva, Emily W. Walton and David T. Takeuchi, "Self-Rated Health among Foreign- and U.S.-Born Asian Americans: A Test of Comparability", Medical Care 45 (2007): 80--87 [As an application of propensity-score matching to a multi-level response]
• David A. Freedman
  • "On Specifying Graphical Models for Causation," UCB Stat. Tech. Rep. 601 [abstract, pdf]
  • Statistical Models and Causal Inference: A Dialogue with the Social Sciences [blurb]
• Anne Gegout-Petit and Daniel Commenges, "A general definition of influence between stochastic processes", arxiv:0905.3619
• Glymour and Cooper (eds.), Computation, Causation and Discovery
• Adam Glynn and Kevin Quinn, "Non-parametric Mechanisms and Causal Modeling" [PDF preprint]
• Jorge Goncalves and Sean Warnick, "Dynamical Structure Functions for the Estimation of LTI Networks with Limited Information", q-bio.MN/0610008 [LTI = "linear, time-invariant"]
• Alison Gopnik and Laura Schulz (eds.), Causal Learning: Psychology, Philosophy and Computation
• James B. Grace, Structural Equation Modeling and Natural Systems [Blurb]
• Stefan Haufe, Guido Nolte, Klaus-Robert Mueller and Nicole Kraemer, "Sparse Causal Discovery in Multivariate Time Series", arxiv:0901.1234 [I am not altogether happy with defining "causes" as "has a non-zero coefficient in a vector autoregression"...]
• Jeffrey Haydu, "Reversals of fortune: path dependency, problem solving, and temporal cases", Theory and Society 39 (2010): 25--48
• Yang-Bo He and Zhi Geng, "Active Learning of Causal Networks with Intervention Experiments and Optimal Designs", Journal of Machine Learning Research 9 (2008): 2523--2547
• Jennifer L. Hill, "Bayesian nonparametric modeling for causal inference", Journal of Computational and Graphical Statistics 20 (2011): 217--240 [Abstract doesn't address issues of identifiability, or the causation/prediction difference]
• Kevin D. Hoover, Causality in Macroeconomics
• Kosuke Imai, Luke Keele, and Teppei Yamamoto, "Identification, Inference and Sensitivity Analysis for Causal Mediation Effects", Statistical Science 25 (2010): 51--71
• Kosuke Imai, Gary King and Elizabeth Stuart, "Misunderstandings among Experimentalists and Observationalists about Causal Inference" [PDF pre-print]
• Katsuhiko Ishiguro, Nobuyuki Otsu, Max Lungarella and Yasuo Kuniyoshi, "Comparison of nonlinear Granger causality extensions for low-dimensional systems", Physical Review E 77 (2008): 036217
• Michael Jachan, Kathrin Henschel, Jakob Nawrath, Ariane Schad, Jens Timmer and Bjorn Schelter, "Inferring direct directed-information flow from multivariate nonlinear time series", Physical Review E 80 (2009): 011138
• Dominik Janzing, Xiaohai Sun and Bernhard Schölkopf, "Distinguishing Cause and Effect via Second Order Exponential Models", arxiv:0910.5561
• David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier, "Automatic Identification of Quasi-Experimental Designs for Discovering Causal Knowledge", SIGKDD 2008 [PDF reprint]
• Jack Katz, "From How to Why: On Luminous Description and Causal Inference in Ethnography"
  • "Part I", Ethnography 2 (2001): 443--473 [PDF reprint]
  • "Part II", Ethnography 3 (2002): 63--90 [PDF reprint]
• Alon Keinan, Ben Sandbank, Claus C. Hilgetag, Isaac Meilijson and Eytan Ruppin, "Fair Attribution of Functional Contribution in Artificial and Biological Networks", Neural Computation 16 (2004): 1887--1915
• Manabu Kuroki, "Bounds on average causal effects in studies with a latent response variable", Metrika 61 (2005): 63--71
• Manabu Kuroki, Zhihong Cai, Hiroki Motogaito "The Graphical Identification for Total Effects by using Surrogate Variables", UAI 2005, arxiv:1207.1392
• Vincent Lariviere, Yves Gingras, "The impact factor's Matthew effect: a natural experiment in bibliometrics", arxiv:0908.3177
• Judith J. Lok
  • "Mimicking counterfactual outcomes for the estimation of causal effects", math.ST/0409045
  • "Statistical modelling of causal effects in continuous time", Annals of Statistics 36 (2008): 1464--1507, math.ST/0410271
• Daniele Marinazzo, Mario Pellicoro and Sebastiano Stramaglia, "Nonlinear parametric model for Granger causality of time series", Physical Review E 73 (2006): 066216 = cond-mat/0602183
• Conor Mayo-Wilson, "The Problem of Piecemeal Induction", Philosophy of Science 78 (2011): 864--874
• Vaughn R. McKim and Stephen P. Turner (ed.), Causality in Crisis? Statistical Methods and the Search for Causal Knowledge in the Social Sciences
• K. Mengersen, S. A. Moynihan, R. L. Tweedie, "Causality and Association: The Statistical and Legal Approaches", arxiv:0710.4459
• Aviv Nevo, Adam M. Rosen, "Identification With Imperfect Instruments", The Review of Economics and Statistics 94 (2012): 659--671
• Judea Pearl, "On Measurement Bias in Causal Inference", UAI 2010, arxiv:1203.3504
• Jonas Peters, Dominik Janzing and Bernhard Schökopf, "Causal Inference on Discrete Data using Additive Noise Models", arxiv:0911.0280
• Adam Przeworski, "Is the Science of Comparative Politics Possible?" [PDF preprint. On drawing causal conclusions from natural "quasi-experiments".]
• Roland R. Ramsahai,
  • "Causal Bounds and Instruments", UAI 2007, arxiv:1206.5262
  • "Causal Bounds and Observable Constraints for Non-deterministic Models", Journal of Machine Learning Research 13 (2012): 829--848
• Federica Russo, "Correlational data, causal hypotheses, and validity", phil-sci/8349
• Federica Russo and Jon Williamson, "Generic versus Single-case Causality: the Case of Autopsy", phil-sci/5148
• Anil K. Seth and Gerald M. Edelman, "Distinguishing Causal Interactions in Neural Populations", Neural Computation 19 (2007): 910--933
• Glenn Shafer, The Art of Causal Conjecture [Bought from an on-line bookstore which gave the title as The Art of Casual Conjecture; a book which should be written. Reviwed by Glymour (PDF)]
• Linda Sommerlade, Michael Eichler, Michael Jachan, Kathrin Henschel, Jens Timmer, and Bjorn Schelter, "Estimating causal dependencies in networks of nonlinear stochastic dynamical systems", Physical Review E 80 (2009): 051128
• Allison J. Sovey and Donald P. Green, "Instrumental Variables Estimation in Political Science: A Readers' Guide", American Journal of Political Science 55 (2011): 188--200 [PDF preprint]
• Elizabeth A. Stuart, "Matching Methods for Causal Inference: A Review and a Look Forward", Statistical Science 25 (2010): 1--21, arxiv:1010.5586
• Ioannis Tsamardinos, Sofia Triantafillou, Vincenzo Lagani, "Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies", Journal of Machine Learning Research 13 (2012): 1097--1157
• C. Uhler, G. Raskutti, B. Yu, Peter Bühlmann, "Geometry of faithfulness assumption in causal inference", arxiv:1207.0547
• Mark J. van der Laan, "Causal Inference for Networks", UC Berkeley Biostatistics working paper no. 300 (2012)
• Mark J. van der Laan and Sherri Rose, Targeted Learning: Causal Inference for Observational and Experimental Data [Blurb]
• P. F. Verdes, "Assessing causality from multivariate time series", Physical Review E 72 (2005): 026222

To write:
• CRS, "Causality in Models of Dynamics"