Abstract: Analyses of causal mediation are often complicated by treatment-induced confounders of the mediator-outcome relationship. In the presence of such confounders, the natural direct and indirect effects of treatment on the outcome, into which the total effect can be additively decomposed, are not identified. An alternative but similar set of effects, known as randomized intervention analogues to the natural direct effect (R-NDE) and the natural indirect effect (R-NIE), can still be identified in this situation, but existing estimators for these effects require a... Read more about Xiang Zhou presents "Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-Residuals Approach"

Abstract: How to estimate heterogeneity, e.g. the effect of some variable differing across observations, is a key question in political science. Methods for doing so make simplifying assumptions about the underlying nature of the heterogeneity to draw reliable inferences. This paper allows a common way of simplifying complex phenomenon (placing observations with similar effects into discrete groups) to be integrated into regression analysis. The framework allows researchers to (i) use their prior knowledge to guide which groups are permissible and (ii) appropriately... Read more about Max Goplerud presents "Modelling Heterogeneity Using Bayesian Structured Sparsity"

Abstract: Heterogeneous effects are ubiquitous in the social sciences and are often an important component of---and means of assessing---theoretical arguments. A common strategy to assess this heterogeneity is to include a single multiplicative interaction term between the treatment and a hypothesized effect moderator in a regression model. In this paper, we show how inferences about interactions under this approach are highly sensitive to modeling choices about how the effect modifier interacts with other covariates, an issue almost never discussed in practice.  We...

Read more about Matt Blackwell presents "On Model Dependence in the Estimation of Interactive Effects"

Covariate adjustment is often used for estimation of population average causal effects (ATE). In recent years graphical rules have been derived for determining, from a causal diagram, all covariate adjustment sets. Restricting attention to causal linear models, a very recent article introduced two graphical criterions: one to compare the asymptotic variance of linear regression estimators that control for certain distinct adjustment sets and a second to identify the optimal adjustment set that provides the smallest asymptotic variance. In this talk, I will show that the same graphical... Read more about Andrea Rotnitzky presents "Efficient adjustment sets for population average causal effect estimation in graphical models"