COPSS Distinguished Achievement Award and Lectureship

Forty years ago, the following disciplines had their own languages, opinions and idiosyncrasies re causal inference: philosophy, computer science, sociology, psychology, statistics, epidemiology, political science, and economics. Today all speak a common language so new methodologies rapidly cross fertilize. Top journals have gone from knee-jerk rejection to active solicitation of articles in the area. The rapid development of the field has been driven by:
1.End of the historical suppression of causal language in statistics and medicine (aside from randomized clinical trials)
2.The internet making cross disciplinary understanding and collaboration easy
3.The need for individualized treatment regimes in Medicine
4. Tech companies realizing that optimizing profits depended on causal interventions rather than just prediction
5.The development of causal graphs by Spirtes, Glymour, Scheines and Pearl that offers non-technical users the ability to validly reason about complex causal systems
6.The existence of huge data sets leading to data driven science rather than hypothesis driven science.
In my lecture, I will give a history of statistical methods for causal inference, focusing on methods developed by myself and colleagues. I will explain why the causal methods we developed for the analysis of time varying treatments have had such a large impact for now over 25 years on substantive areas in which confounding by time varying covariates is very strong, as in studies of HIV-infected individuals. In addition, I will describe why these methods are an integral part of the target trial methodology introduced by Miguel Hernan and myself - a methodology that is altering the analytical paradigm for the estimation of causal effects from longitudinal observational data in Medicine.
In more detail, I will review both (i) the role of marginal structural models, structural nested models, and the g-formula in modelling the effects of time-varying treatments and (ii) the development, joint with Andrea Rotnitzky, of doubly and multiply robust estimation of the model parameters. This will be followed by a brief review of ground-breaking causal methods developed by other researchers, centering on the development of proximal inference by Eric Tchetgen Tchetgen and Wang Miao and the contributions of Mark van der Laan. I will conclude with a discussion of the future of causal inference in the coming age of AI.