Causality and Causal Inference

07 Jan 2010 08:44

---

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: Matched sampling methods.

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.]
• Judea Pearl
  • "Causal Inference in Statistics: An Overview", forthcoming in Statistics Surveys 3 (2009): 96--146 [PDF]
  • Causality: Models, Reasoning and Inference
• Hans Reichenbach, The Direction of Time [Comments]
• 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]
• 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):
• 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
• Bertrand Russell
  • The Analysis of Matter
  • Human Knowledge: Its Scope and Limits

Recommended (more specialized):
• Tianjiao Chu and Clark Glymour, "Search for Additive Nonlinear Time Series Causal Models", Journal of Machine Learning Research 9 (2008): 967--991
• 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).]
• J. D. Ramsey, S. J. Hanson, C. Hanson, Y. O. Halchenko, R. A. Poldrack and C. Glymour, "Six Problems for Causal Inference from fMRI" [Thanks to Prof. Glymour for a preprint]
• 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]
• Wesley Salmon
  • Scientific Explanation and the Causal Structure of the World
  • Causality and Explanation
• Herbert Simon, "Causal Ordering and Identifiability"
• Peter Spirtes, "Limits on Causal Inference from Observational Data" [PostScript preprint]
• 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]

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
• 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
• Blalock, Causal Inferences in Nonexperimental Research
• 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
• 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", arxiv:0710.4396
• Rajeev H. Dehejia and Sadek Wahba, "Propensity Score-Matching Methods for Nonexperimental Causal Studies", The Review of Economics and Statistics 84 (2002): 151--161
• 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
• 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
• 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
• 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
• Kevin D. Hoover, Causality in Macroeconomics
• Kosuke Imai, Gary King and Elizabeth Stuart, "Misunderstandings among Experimentalists and Observationalists about Causal Inference" [PDF pre-print]
• 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
• 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
• 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
• 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
• Morgan and Winship, Counterfactuals and Causal Inference: Methods and Principles for Social Research [blurb]
• 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
• 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]
• 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]
• 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
• 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"
• CRS, "Homophily, Contagion, Confounding: Pick Any Three"