Should Causality Be Defined in Terms of Experiments

The paper reviews what has come to be called the Rubin-Holland model of causation. That model takes as its starting point experimentation and defines causation as a hypothetical value: a difference score between the observation of a unit in control and experimental condition. We discuss several different implementations of the model that Rubin and Holland have suggested. We present several other possible implementations with special emphasis of quasi-experiments. Finally, we note that causality can alternatively be better defined in terms of the absence of spuriousness.

[1]  C. Glymour,et al.  STATISTICS AND CAUSAL INFERENCE , 1985 .

[2]  R. Abelson.  Statistics As Principled Argument , 1995 .

[3]  Harrison Si,et al.  Handbook of Research Methods in Social and Personality Psychology: Author Index , 2013 .

[4]  D. Rubin.  ASSIGNMENT TO TREATMENT GROUP ON THE BASIS OF A COVARIATE , 1976 .

[5]  D. Rubin.  Assignment to Treatment Group on the Basis of a Covariate , 1976 .

[6]  J. Stanley.  Quasi-Experimentation , 1965, The School Review.

[7]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[8]  D. Rubin,et al.  Bayesian inference for causal effects in randomized experiments with noncompliance , 1997 .

[9]  P. Suppes.  A Probabilistic Theory Of Causality , 1970 .

[10]  N. Bolger,et al.  Diary methods: capturing life as it is lived. , 2003, Annual review of psychology.

[11]  D. Basu.  Randomization Analysis of Experimental Data: The Fisher Randomization Test , 1980 .

[12]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[13]  J. Heckman.  Sample selection bias as a specification error , 1979 .

[14]  D. Rubin.  [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .

[15]  Stephen G. West,et al.  Causal inference and generalization in field settings: Experimental and quasi-experimental designs. , 2000 .

[16]  D. A. Kenny,et al.  Estimating the effects of social interventions , 1981 .

[17]  D. A. Kenny,et al.  A quasi-experimental approach to assessing treatment effects in the nonequivalent control group design. , 1975 .

[18]  D B Rubin,et al.  Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. , 1991, Biometrics.

[19]  P. Holland.  Causal Inference, Path Analysis and Recursive Structural Equations Models. Program Statistics Research, Technical Report No. 88-81. , 1988 .

[20]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[21]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[22]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[23]  D. Rubin.  Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment , 1980 .

[24]  P. Rosenbaum.  From Association to Causation in Observational Studies: The Role of Tests of Strongly Ignorable Treatment Assignment , 1984 .