Causal Inference Lecture Notes: Covariate Adjustments in Randomized Experiments
Kosuke Imai
Abstract
Kosuke Imai
Abstract
In our previous discussion of classical approaches to randomized experiments, we did not talk about the situations in which we know more about experimental units. Indeed, typically such covariate information is available to experimenters. For example, in social science experiments, the demographic information about subjects may be known to researchers. Here, we consider how we may take an advantage of such information in randomized experiments.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
In our previous discussion of classical approaches to randomized experiments, we did not talk about the situations in which we know more about experimental units. Indeed, typically such covariate information is available to experimenters. For example, in social science experiments, the demographic information about subjects may be known to researchers. Here, we consider how we may take an advantage of such information in randomized experiments.
Key concepts: Covariate, Randomized experiment, Causal inference, Inference, Randomized controlled trial, Computer science, Econometrics, Psychology