Estimating Causality from Observational Data | Jason Bryer

Estimating Causality from Observational Data

119 West 31st Street, New York, NY

The CUNY School of Professional Studies, Data Science and Information Systems department, is hosting a talk by Jason Bryer titled Estimating Causality from Observation Data. You can attend the talk in person at 119 West 31st Street, NY, New York, 10001 or watch the live stream. Please complete this form indicating your interest. Details on attending live or getting the Zoom link will be sent to the email address provided. Light refreshments will be provided for those attending in person.


The use of propensity score methods (Rosenbaum & Rubin, 1983) for estimating causal effects in observational studies or certain kinds of quasi-experiments has been increasing in the social sciences (Thoemmes & Kim, 2011) and in medical research (Austin, 2008) in the last decade. Propensity score analysis (PSA) attempts to adjust selection bias that occurs due to the lack of randomization. Analysis is typically conducted in three phases where in phase I, the probability of placement in the treatment is estimated to identify matched pairs or clusters so that in phase II, comparisons on the dependent variable can be made between matched pairs or within clusters, and phase III, robustness to unobserved covariates is estimated. R (R Core Team, 2023) is ideal for conducting PSA given its wide availability of the most current statistical methods vis-à-vis add-on packages as well as its superior graphics capabilities.

This talk will provide participants with a theoretical overview of propensity score methods as well as illustrations and discussion of PSA applications. Methods used in phase I of PSA (i.e. models or methods for estimating propensity scores) include logistic regression, classification trees, and matching. Discussions on appropriate comparisons and estimations of effect size and confidence intervals in phase II will also be covered. The use of graphics for diagnosing covariate balance as well as summarizing overall results will be emphasized.

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