Naftali Weinberger (Munich Center for Mathematical Philosophy)
Abstract:
Causal mediation techniques are a means for identifying the degree to which a cause influences its effect along particular causal paths. For example, in a model where a cause influences its effect both indirectly via a mediator and directly via factors not included in the model, mediation techniques enable one to measure both direct and indirect effects. Although mediation techniques are widely employed, they are often misunderstood. This is in part due to the long-term influence of Baron and Kenny鈥檚 (1986) treatment of mediation, which applies only to linear models without interaction, and which leads one to develop intuitions about direct and indirect effects that do not generalize to non-parametric causal models. In my talk, I identify and reject three persistent myths about mediation. I argue that such methods: 1. Should not be understood as decomposing the total effect into additive components corresponding to the contributions of the paths; 2. Are not a means for eliminating latent heterogeneity; and 3. Do not require one to appeal to causal concepts other than the counterfactual causal ones built into structural causal models. These points are crucial for understanding mediation effects in any contexts in which they are studied, and have particular applications for studies of fairness and discrimination, in which such effects play an increasingly central role (Ple膷ko and Bareinboim, 2022).
Speaker
Naftali Weinberger is a scientific researcher at the Munich Center for Mathematical Philosophy. His work concerns the use of causal methodology to address foundational questions arising in the philosophy of science as well as questions arising in particular sciences, including: biology, psychometrics, neuroscience, and cognitive science. He currently has two primary research projects 鈥� one on causation in complex dynamical systems and another on the use of causal methods for the analysis of racial discrimination. He is currently trying to convince causal researchers that causal representations are implicitly relative to a particular time-scale and that it is therefore crucial to pay attention to temporal dynamics when designing and evaluating interventions.