© 2003 by Biometrika Trust
Uniform consistency in causal inference
1 Department of Epidemiology and Biostatistics, Harvard University, Boston, Massachusetts 02115, U.S.Arobins{at}hsph.harvard.edu 2 Department of Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A.scheines{at}andrew.cmu.edu ps7z{at}andrew.cmu.edu 3 Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A. larry{at}stat.cmu.edu
There is a long tradition of representing causal relationships by directed acyclic graphs (Wright, 1934).Spirtes (1994), Spirtes et al. (1993) and Pearl & Verma (1991) describe procedures for inferring the presence or absence of causal arrows in the graph even if there might be unobserved confounding variables, and/or an unknown time order, and that under weak conditions, for certain combinations of directed acyclic graphs and probability distributions, are asymptotically, in sample size, consistent. These results are surprising since they seem to contradict the standard statistical wisdom that consistent estimators of causal effects do not exist for nonrandomised studies if there are potentially unobserved confounding variables. We resolve the apparent incompatibility of these views by closely examining the asymptotic properties of these causal inference procedures. We show that the asymptotically consistent procedures are ‘pointwise consistent’, but ‘uniformly consistent’ tests do not exist. Thus, no finite sample size can ever be guaranteed to approximate the asymptotic results. We also show the nonexistence of valid, consistent confidence intervals for causal effects and the nonexistence of uniformly consistent point estimators. Our results make no assumption about the form of the tests or estimators. In particular, the tests could be classical independence tests, they could be Bayes tests or they could be tests based on scoring methods such as BIC or AIC. The implications of our results for observational studies are controversial and are discussed briefly in the last section of the paper. The results hinge on the following fact: it is possible to find, for each sample size n, distributions P and Q such that P and Q are empirically indistinguishable and yet P and Q correspond to different causal effects.
Key Words: Causation; Confounding; Directed acyclic graph
Received September 2000. Revised March 2003
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