UCPH Statistics Seminar: Causal Inference with Unmeasured Confounding

1. Georgia Papadogeorgou
   
   Title: Unmeasured spatial confounding
   
   Abstract: Spatial confounding has different interpretations in the spatial and causal inference literatures. I will begin this talk by clarifying these two interpretations. Then, seeing spatial confounding through the causal inference lens, I discuss an approach to account for unmeasured variables that are spatially structured when we are interested in estimating causal effects. In this approach, we aim to bridge the spatial and causal inference literatures by estimating causal effects in the presence of unmeasured spatial variables using outcome modeling tools that are popular in spatial statistics. Motivated by the bias term of commonly-used estimators in spatial statistics, we propose an affine estimator that addresses this deficiency. I will discuss that estimation of causal parameters in the presence of unmeasured spatial confounding can only be achieved under an untestable set of assumptions. We provide one such set of assumptions which describe how the exposure and outcome of interest relate to the unmeasured variables.
   
   
2. Joseph Antonelli
   
   Title: Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy
   
   Abstract: In New York City, neighborhood policing was adopted at the police precinct level over the years 2015-2018, and it is of interest to both (1) evaluate the impact of the policy, and (2) understand what types of communities are most impacted by the policy, raising questions of heterogeneous treatment effects. We develop novel statistical approaches that are robust to unmeasured confounding bias to study the causal effect of policies implemented at the community level. We find that neighborhood policing decreases discretionary arrests in certain areas of the city, but has little effect on crime or racial disparities in arrest rates.