Source Themes

Discussion of the manuscript: 'Spatial+ a novel approach to spatial confounding' by Dupont, Wood and Augustin

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. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an …

Clarifying Selection Bias in Cluster Randomized Trials

Soft tensor regression

Estimation of tensor regression coefficients on a soft version of the PARAFAC approximation.

Propensity Score Weighting for Causal Subgroup Analysis

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

Bipartite Causal Inference with Interference

Extending causal inference in the presence of interference to bipartite settings where the interventional units are different from the outcome units.

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.

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

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.