Explore projects in Causal Inference!
Projects in advancing causal inference focus on developing and refining methods to better understand cause-and-effect relationships in complex social, behavioral, and developmental contexts. These projects aim to strengthen the validity of causal claims by improving study design, measurement, and analytic strategies beyond traditional correlational approaches. Researchers may explore experimental and quasi-experimental designs, instrumental variables, propensity score matching, regression discontinuity, natural experiments, and longitudinal modeling techniques. Many projects also integrate innovations in computational methods, machine learning, and sensitivity analysis to address confounding, selection bias, and missing data. By advancing tools for drawing credible causal conclusions, these projects enhance the rigor and impact of research across diverse disciplines.
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- Causal Inference for Incomplete and Heterogeneous Multisite and Blocked ExperimentsProject Abstract/Summary This research project will expand randomization-based causal inference methodology for complex blocked and multisite randomized control trials. Researchers aiming to learn about the causal effects of treatments or interventions often use blocked and multisite trials. With these designs, experimental units are categorized into blocks or sites and then independent experiments occur within each block or site. The designs are especially common in the social and behavioral sciences; currently, however, they have some limitations. This project will fill many… Read more: Causal Inference for Incomplete and Heterogeneous Multisite and Blocked Experiments
- A Design-based Riesz Representation Estimation Approach for Randomized ExperimentsProject Abstract/Summary This research project will develop and investigate methods for the estimation of causal effects in randomized experiments. Randomized experiments are used as an empirical method by scientists and researchers in a wide range of fields, both in the public and private sectors. The method is appreciated by researchers because it allows for conclusions that are credible and robust. However, randomized experiments cannot be used to investigate complex settings, such as when study participants interact with each other, because… Read more: A Design-based Riesz Representation Estimation Approach for Randomized Experiments
- Sensitivity Analysis Tools for Quasi-Experimental DesignsProject Abstract/Summary This research project will develop new theory and methods for assessing the sensitivity of causal inferences to violations of underlying assumptions. Applied causal inference work in the social, behavioral, and economic sciences often use a handful of methods commonly known as “quasi-experimental” designs. However, the validity of these methods requires strong assumptions about the data-generating process, many of which are difficult to defend in practice. What if these assumptions are false? In such cases, sensitivity analyses play an… Read more: Sensitivity Analysis Tools for Quasi-Experimental Designs