Causality and Causal Inference

23 Dec 2011 14:09

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There is unfortunately no accepted name for the scientific study of causality, or of methods for inferring it. "Etiology" suggests itself, but it's already taken...

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
• Judea Pearl
  • "Causal Inference in Statistics: An Overview", forthcoming in Statistics Surveys 3 (2009): 96--146 [PDF]
  • 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.]
• Tianjiao Chu and Clark Glymour, "Search for Additive Nonlinear Time Series Causal Models", Journal of Machine Learning Research 9 (2008): 967--991
• 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]
• David Galles and Judea Pearl
  • "Axioms of Causal Relevance" [preprint]
  • "An Axiomatic Characterization of Causal Counterfactuals" [preprint]
  • "Testing Identifiability of Causal Effects" [preprint]
• Clark Glymour, "When Is a Brain Like the Planet?", Philosophy of Science 74 (2007): 330--347
• Clive Granger [His original paper on what has come to be called "Granger causality" is 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 don't feel energetic enough right now to either find it in my filing cabinet or look up the exact citation.]
• Dominik Janzing, "On causally asymmetric versions of Occam's Razor and their relation to thermodynamics", arxiv:0708.3411
• Kevin T. Kelly and Conor Mayo-Wilson, "Causation, Retraction, Simplicity, and Truth" [Unpublished; thanks to Kevin for a preprint]
• Gustavo Lacerda, Peter Spirtes, Joseph Ramsey and Patrik O. Hoyer, "Discovering Cyclic Causal Models by using Independent Components Analysis" [PDF draft via Gustavo]
• 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]
• Maxim Raginsky, "Directed information and Pearl's causal calculus", arxiv:1110.0718
• 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
• Wesley Salmon
  • Scientific Explanation and the Causal Structure of the World
  • Causality and Explanation
• 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
• Robert E. Tillman, Arthur Gretton and Peter Spirtes, "Nonlinear Directed Acyclic Structure Learning with Weakly Additive Noise Models" [Thanks to Prof. Spirtes for a preprint]
• Halbert White and Karim Chalak, "A Unified Framework for Defining and Identifying Causal Effects" [Preprint of Jan. 30, 2006; thanks to D. R. White for letting me know about this paper and sending me a later version. Submitted to Econometrica]
• 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 [1962, so technically obsolete, but interesting to see just how many of the pieces that came together in the early 1990s were in place much earlier. One of his procedures seems to be something like a cross between an instrumental variable and propensity score matching.]
• David Hume
• ibn Rushd (= Averroes)
  • Tahafut al-Tahafut [Which, needless to say, I've only read in translation]
  • Barry Kogan, Averroes and the Metaphysics of Causation
• 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]
• Hans Reichenbach, The Direction of Time [Comments]
• Bertrand Russell
  • The Analysis of Matter
  • Human Knowledge: Its Scope and Limits

Modesty forbids me to recommend:
• 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
• Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos, "Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification"
  • "Part I: Algorithms and Empirical Evaluation", Journal of Machine Learning Research 11 (2010): 171--234
  • "Part II: Analysis and Extensions", Journal of Machine Learning Research 11 (2010): 235--284
• P. O. Amblard and O. J. J. Michel, "On directed information theory and Granger causality graphs", arxiv:1002.1446
• Nicola Ancona, Daniele Marinazzo and Sebastiano Stramaglia, "Extending Granger causality to nonlinear systems", physics/0405009
• Nihat Ay, "A Refinement of the Common Cause Principle", SFI Working Paper 08-01-001 [PDF]
• 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"]
• 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
• Facuno Bromberg and Dimitris Margaritis, "Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation", Journal of Machine Learning Research 10 (2009): 301--340
• Joseph Keim Campbell, Michael O'Rourke and Harry S. Silverstein (eds.), Causation and Explanation [Blurb]
• Nancy Cartwright, Hunting Causes and Using Them: Approaches in Philosophy and Economics [blurb. Extremely harsh critiques by Pearl and Glymour ("All of her critical claims are false or at best fractionally true")]
• 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
• John Collins, Ned Hall, L.A. Paul (eds.), Causation and Counterfactuals [Forthcoming]
• 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]
• Ehtibar N. Dzhafarov, Janne V. Kujala, "Selectivity in Probabilistic Causality: Drawing Arrows from Inputs to Stochastic Outputs", arxiv:1108.3074
• Frederick Eberhardt and Richard Scheines, "Interventions and Causal Inference", phil-sci/2944
• Ellery Eells, Probabilistic Causality
• 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
• Adam Elga, "Isolation and Folk Physics", phi-sci/2678 [Ordinary notions of causality as approximations to real physics, under conditions of near-independence]
• 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]
• Galavotti (ed.), Stochastic Causality
• Anne Gegout-Petit and Daniel Commenges, "A general definition of influence between stochastic processes", arxiv:0905.3619
• Clark Glymour, "Rabbit Hunting", Synthese 121 (1999): 55--78 [PDF reprint]
• 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]
• Joseph Y. Halpern and Judea Pearl, "Causes and Explanations: A Structural-Model Approach", "Part I: Causes", cs.AI/0011012, and "Part II: Explanations," cs.AI/0208034
• 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
• Joe Henson, "Comparing causality principles", Studies in History and Philosophy of Modern Physics 36 (2005): 519--543
• Jennifer L. Hill, "Bayesian nonparametric modeling for causal inference", Journal of Computational and Graphical Statistics online before print (2010)
• 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
• Samantha Kleinberg, An Algorithmic Enquiry Concerning Causality [Ph.D. thesis, NYU, 2010; PDF]
• Manabu Kuroki, "Bounds on average causal effects in studies with a latent response variable", Metrika 61 (2005): 63--71
• Vincent Lariviere, Yves Gingras, "The impact factor's Matthew effect: a natural experiment in bibliometrics", arxiv:0908.3177
• Junning Li, Z. Jane Wang, "Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm", Journal of Machine Learning Research 10 (2009): 475--514
• Stanley Lieberson, "The Big Broad Issues in Society and Social History: Application of a Probabilistic Perspective", pp. 359--385 in Vaughn R. McKim and Stephen P. Turner (eds.), Causality in Crisis? Statistical Methods and the Search for Causal Knowledge in the Social Sciences [PDF reprint]
• Judith J. Lok
  • "Mimicking counterfactual outcomes for the estimation of causal effects", math.ST/0409045
  • "Statistical modelling of causal effects in continuous time", math.ST/0410271
• 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
• 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
• 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
• Peter Menzies, "A Structural Equations Account of Negative Causation", phil-sci/2962
• John D. Norton, "Causation as Folk Science," phil-sci/1214
• Farid Nouioua, "Why did the accident happen? A norm-based reasoning approach", cs.AI/0610015
• L. A. (Laurie) Paul
• David T. Pegg, "Causality in quantum mechanics", Physics Letters A 349 (2006): 411--414
• Jean-Philippe Pellett and Andre Elisseeff, "Using Markov Blankets for Causal Structure Learning", Journal of Machine Learning Research 9 (2008): 1295--1342
• Jonas Peters, Dominik Janzing and Bernhard Schökopf, "Causal Inference on Discrete Data using Additive Noise Models", arxiv:0911.0280
• Huw Price and Richard Corry (eds.), Causation, Physics, and the Constitution of Reality: Russell's Republic Revisited
• Adam Przeworski, "Is the Science of Comparative Politics Possible?" [PDF preprint. On drawing causal conclusions from natural "quasi-experiments".]
• Miklós Rédei and Stephen J. Summers, "Remarks on Causality in Relativistic Quantum Field Theory", quant-ph/0302115
• Eva Riccomagno, Jim Q. Smith
  • "Algebraic causality: Bayes nets and beyond", arxiv:0709.3377
  • "The causal manipulation of chain event graphs", 0709.3380
• 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)]
• Ilya Shpitser, Judea Pearl, "Complete Identification Methods for the Causal Hierarchy", Journal of Machine Learning Research 9 (2008): 1941--1979 ["We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; cause-effect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple "parallel worlds" and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy"]
• Silva, Scheines, Glymour and Spirtes, "Learning the Structure of Linear Latent Variable Models", Journal of Machine Learning Research 7 (2006): 191--246 [open access]
• 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]
• Dan Sperber, David Premack and Ann James Premack (eds.), Causal Cognition: A Multidisciplinary Debate
• Peter Spirtes
  • "Graphical models, causal inference, and econometric models", Journal of Economic Methodology 12 (2005): 1--33 [PDF]
  • "Introduction to Causal Inference", Journal of Machine Learning Research 11 (2010): 1643--1662
• Elizabeth A. Stuart, "Matching Methods for Causal Inference: A Review and a Look Forward", Statistical Science 25 (2010): 1--21, arxiv:1010.5586
• Patrick Suppes
  • Patrick Suppes, Scientific Philosopher
  • A Probabilistic Theory of Causality
  • Representation and Invariance
• G. A. Svechnikov, Causality and the Relation of States in Physics
• Mark J. van der Laan and Sherri Rose, Targeted Learning: Causal Inference for Observational and Experimental Data [Blurb]
• Tyler J. VanderWeele and James M. Robins
  • "Minimal sufficient causation and directed acyclic graphs", Annals of Statistics 37 (2009): 1437--1465
  • "Properties of Monotonic Effects on Directed Acyclic Graphs", Journal of Machine Learning Research 10 (2009): 699--718
  • "Signed directed acyclic graphs for causal inference", Journal of the Royal Statistical Society B 72 (2010): 111--127
• P. F. Verdes, "Assessing causality from multivariate time series", Physical Review E 72 (2005): 026222
• Brad Weslake, "Common Causes and The Direction of Causation", phil-sci 2383
• 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
• Phillip Wolff, "Representing Causation", phil-sci/3177
• James Woodward, Making Things Happen: A Theory of Causal Explanation [Review by Glymour]
• Raanan Yehezkel, Boaz Lerner, "Bayesian Network Structure Learning by Recursive Autonomy Identification", Journal of Machine Learning Research 10 (2009): 1527--1570
• Jiji Zhang, "Causal Reasoning with Ancestral Graphs", Journal of Machine Learning Research 9 (2008): 1437--1474
• Zhang Jiji and Peter Spirtes, "Detection of Unfaithfulness and Robust Causal Inference", phil-sc/3188

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