Mimicking counterfactual outcomes for the estimation of causal effects

Details Mimicking counterfactual outcomes for the estimation of causal effects Mimicking counterfactual outcomes for the estimation of causal effects

2004-09-03

arxiv.org/abs/math/0409045v1

Mathematics

Statistics Theory

60 pages

Scientific paper

Large observational studies have become commonplace in medical research. Treatment may be adapted to covariates at several instances without a fixed protocol. Estimation or even definition of treatment effect is difficult in that case. Treatment influences covariates, which influence treatment, which influences covariates, etcetera. To distinguish between these options, even the famous time-dependent Cox-model cannot be used. Robins (1992, 1998), Keiding (1999) and Lok (2001, 2004) study Structural Nested Models to estimate treatment effects even in this difficult setting. Their methods are based on so-called counterfactuals: the outcome a patient would have had if treatment was withheld after a certain time. It is clearly impossible for these outcomes to be observed in all patients. Yet we will show how counterfactual thinking is a very helpful tool to study estimation of treatment effect in the presence of time-dependent covariates. Previous work on these models was usually based on the assumption that the correct model combined with observations made it possible to calculate all counterfactuals for each patient. This assumption was considered not plausible, since it assumes the exact same treatment effect for each patient. This paper provides the cornerstone for the relaxation of treatment effects in e.g. Robins (1992, 1998) or Keiding (1999) in that, at least if there is no censoring, the assumption that counterfactuals are connected with the observed data in a deterministic way is not necessary. We hope that this will contribute to the discussion about causal reasoning.

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