The causal inference seminar series is co-sponsored by the Departments of Epidemiology and Biostatistics. Speakers will be drawn from the health sciences, social sciences and statistics, from faculty in the Boston area and more broadly. The series will include both more theoretical sessions oriented towards methods development and more applied sessions which examine and critically evaluate what methods from causal inference contribute to science. All students and faculty are invited to attend and participate.

Michael E. Sobel, Ph.D.
Professor, Department of Sociology, Columbia University

"Does Marriage Boost Men's Wages?: Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data"

ABSTRACT: Social scientists have generated a large and inconclusive literature on the effect(s) of marriage on men's wages. Researchers have hypothesized that the wage premium enjoyed by married men may reflect both a tendency for more productive men to marry and an effect of marriage on productivity. To sort out these explanations, researchers have used fixed effects regression models for panel data to adjust for selection on unobserved time-invariant confounders, interpreting coefficients for the time-varying marriage variables as effects. However, they did not define these effects or give conditions under which the regression coefficients would warrant a causal interpretation. Consequently, they failed to appropriately adjust for important time-varying confounders and misinterpreted their results. Regression models for panel data with unobserved time-invariant confounders are also widely used in many other policy-relevant contexts and the same problems arise there. This article draws on recent statistical work on causal inference with longitudinal data to clarify these problems and help researchers use appropriate methods to model their data. A basic set of treatment effects is defined and used to define derived effects. Causal models for panel data with unobserved time-invariant confounders are defined and the treatment effects are reexpressed in terms of these models. Ignorability conditions under which the parameters of the causal models are identified from the regression models are given. Even when these hold, a number of interesting and important treatment effects are typically not identified.

Richard Emsley, Ph.D.
Lecturer in Biostatistics, Centre for Biostatistics, University of Manchester, United Kingdom

"Causal Inference and Personalized Medicine: Bringing Mechanisms Evaluation to the Fore"

ABSTRACT: The underlying concept of personalised medicine is treatment-effect heterogeneity and it is intrinsically dependent on an understanding of treatment-effect mechanisms (effects on therapeutic targets that mediate the effect of the treatment on clinical outcomes). In experimental medicine there is a need for novel clinical trial designs for the joint evaluation of treatment efficacy, the utility of predictive markers as indicators of treatment efficacy, and the meditational mechanisms proposed as the explanation of these effects. We illustrate the potential of the predictive biomarker-stratified design, together with baseline measurement of all known prognostic markers, to enable us to evaluate both the utility of the predictive biomarker in such a stratification and, perhaps more importantly, to estimate how much of the treatment's effect is actually explained by changes in the putative mediator. We call this a biomarker stratified efficacy and mechanisms evaluation (BS-EME) trial. The primary analysis strategy involves the use of structural mean models, essentially using the treatment by predictive biomarker interaction as an instrumental variable – a refined, subtle and potentially more powerful use of Mendelian randomisation. We discuss the limitations of such as approach in observational studies, and highlight some current opportunities for pursuing this area within the UK research environment.