Using an accessible approach perfect for social and behavioral science students (requiring minimal use of matrix and vector algebra), Holmes examines how propensity scores can be used to both reduce bias with different kinds of quasi-experimental designs and fix or improve broken experiments. This unique book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of social and behavioral science disciplines.

Causal Inference Using Counterfactual Designs

This chapter reviews how counterfactual designs are used to reduce problems of causal inference. It identifies challenges in developing counterfactual designs. It addresses the role of counterfactual evidence and randomization in inference. It discusses trade-offs between different designs and issues unresolved by counterfactual design. It also introduces propensity matching and propensity weighting and their roles in causal inference.

Controlled Experiments

As discussed in Chapter 1, controlled experiments provide a model for causal inference. They start out with the cases in each group having the same average characteristics. This is a result of random assignment of cases to each group. Any differences after the intervention are attributed to the intervention, provided the two groups continue to have the same experiences during the ...

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