## Causal Inference

*06 Sep 2013 12:13*

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

- "Causal Inference in Statistics: An Overview",
Statistics Surveys
- 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] - Franklin M. Fisher, "A Correspondence Principle for Simultaneous
Equation
Models", Econometrica
**38**(1970): 73--92 [When are simultaneous systems of equations legitimate limits of models of time-evolution? And when does it make sense to calculate causal effects in simultaneous-equation models by "surgery"?] - 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]

- Michael E. Sobel
- "Does Marriage Boost Men's Wages? Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data", Journal of the American Statistical Association
**107**(2012): 521--529 - "What Do Randomized Studies of Housing Mobility Demonstrate?", Journal of the American Statistical Association
**101**(2006): 1398--1407

- "Does Marriage Boost Men's Wages? Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data", Journal of the American Statistical Association
- Peter Spirtes, "Limits on Causal Inference from Observational Data" [PostScript preprint; PDF]
- 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
- 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]

- "Part I", Ethnography
- 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 - Combining Causal Theories and Dividing Scientific Labor [Ph.D. thesis, CMU Philosophy Dept., 2012; thanks to Dr. Mayo-Wilson for a copy]

- "The Problem of Piecemeal Induction",
Philosophy of Science
- 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"