Parallel ensemble methods for causal direction inference

Matching Methods for Causal Inference

Causal Inference by Direction of Information

We focus on data-driven causal inference. In particular, we propose a new principle for causal inference based on algorithmic information theory, i.e. Kolmogorov complexity. In a nutshell, we determine how much information one data object gives about the other, and vice versa, and identify the most likely causal direction by the strongest direction of information. To apply this principle in pra...

Complete Identi cation Methods for Causal Inference

of the Dissertation Complete Identification Methods for Causal Inference

Opiates for the Matches: Matching Methods for Causal Inference

In recent years, there has been a burst of innovative work on methods for estimating causal effects using observational data. Much of this work has extended and brought a renewed focus on old approaches such as matching, which is the focus of this review. The new developments highlight an old tension in the social sciences: a focus on research design versus a focus on quantitative models. This ...

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...