Source Themes

Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. The proposed framework extends many existing estimators introduced in the causal inference literature to high-dimensional settings. We …

Propensity Score Weighting for Causal Subgroup Analysis

Causal inference for subgroup analysis and the Connect-S plot.

Discussion of 'Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects' by Hahn, Murray and Carvalho

Mitigating Unobserved spatial confounding when estimating the effect of supermarket access on cardiovascular disease deaths

Addressing confounding bias from unmeasured spatial variables using mixed models.

A Causal Exposure Response Function with Local Adjustment for Confounding: A study of the health effects of long-term exposure to low levels of fine particulate matter

Causal exposure-response curve estimation with local confonding adjustment. Different variables confound the exposure-response relationship at different exposure levels.

Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions

A review of the literature on the health effects of exposure to low levels of particulate matter.

Discussion of 'Penalized Spline of Propensity Methods for Treatment Comparison'

Causal inference with interfering units for cluster and population level treatment allocation programs

Causal inference with interference for realistic treatment allocation programs. Evaluating the comparitive effectiveness of power plant emission reduction strategies for reducing ambient ozone concentrations.

Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching

Incorporating geographical distance in a propensity score matching approach to account for unmeasured confounding by spatial variables.