Causality and Causal Inference

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.

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]

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

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]

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

CRS, "Causality in Models of Dynamics"
CRS, "Homophily, Contagion, Confounding: Pick Any Three"

Notebooks

Causality and Causal Inference