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Tuesday, January 25, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)
Speaker: Daniel McCaffrey (ETS)
Title: Nonrandom Samples and Causal Inference
Abstract: Causal inferences, i.e., estimates of how a treatment or intervention affects outcomes, are of great interest in many fields. There are many causal modeling methods for estimating causal effects from observational data that attempt to adjust for potential biases due to the differences between individuals receiving different treatments in natural settings. These methods nearly universally implicitly assume the data are a random sample from the population of interest. Nonrandom samples occur when using survey samples or because of nonresponse or study attrition. Weighting commonly is proposed for adjusting the nonrandom samples to be representative of the population of interest. There are questions about how to use these sampling (or nonresponse or attrition) weights with causal modeling techniques. Authors have explored the issue but the advice is somewhat conflicting. In this talk, I will demonstrate that under certain assumptions combining sampling (or nonresponse or attrition) weights with inverse-probability-of-treatment weights can yield consistent estimates of causal effects for the entire population of interest. I show that different assumptions lead to different recommendations for how to use the sampling (or nonresponse or attrition) weights. I also show that using simulate data to study how to use such weights without a theoretical foundation can lead to confusing conclusions.