16 Jan Causal Inference from Observational Studies: Matching
1:00 pm - 2:30 pm
statistics seminar | level: intermediate |
for questions related to this event, contact firstname.lastname@example.org
affiliation: Ghent University
This seminar introduces you to Matching as a pre-processing step for reducing imbalance and model dependency in the data and thus resulting in unbiased estimates of causal treatment effects using observational data. You will be introduced to different matching methods including matching based on Propensity Scores (PS). You will be introduced to strategies for evaluating matching and performing outcome analysis. You will also be provided with an understanding of the strengths and limitations of Matching methods especially matching based on PS and some best practices. Some examples from the literature will be presented with an illustration of the different steps using the R software.
Bob Trenwith: Causality-Inferring Causal Effects from Data
Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.” R by Joy Shi and Sean McGrath
Gary King and Richard Nielsen. 2019. “Why Propensity Scores Should Not Be Used for Matching.” Political Analysis, 27, 4, Pp. 435-454.