Causal inference in collaboration networks using propensity score methods

Propensity Score Methods for Causal Inference with the PSMATCH Procedure

In a randomized study, subjects are randomly assigned to either a treated group or a control group. Random assignment ensures that the distribution of the covariates is the same in both groups and that the treatment effect can be estimated by directly comparing the outcomes for the subjects in the two groups. In contrast, subjects in an observational study are not randomly assigned. In order to...

Causal Moderation Analysis Using Propensity Score Methods Authors and Affiliations:

Propensity Score-Based Methods versus MTE-Based Methods in Causal Inference: Identification, Estimation, and Application.

Since the seminal introduction of the propensity score by Rosenbaum and Rubin, propensity-score-based (PS-based) methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the propensity score approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For situations where ...

Covariate selection and propensity score specification in causal inference

This thesis makes contributions to the statistical research field of causal inference in observational studies. The results obtained are directly applicable in many scientific fields where effects of treatments are investigated and yet controlled experiments are difficult or impossible to implement. In the first paper we define a partially specified directed acyclic graph (DAG) describing the i...

Uncertain Neighbors: Bayesian Propensity Score Matching for Causal Inference

In this paper we compare the performance of standard nearest-neighbor propensity score matching with that of an analogous Bayesian propensity score matching procedure. We show that the Bayesian approach has several advantages, including that it makes better use of available information, since it makes less arbitrary decisions about which observations to drop and which ones to keep in the matche...