Chris Muris

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About

Chris Muris is an econometrician whose research centers on the identification and estimation of causal effects using panel data, with a particular focus on interaction effects and nonlinear models. His most-cited work, "Estimating interaction effects with panel data" (2022), tackles a fundamental challenge in applied microeconometrics: how to consistently estimate interaction effects in linear panel models when the time dimension is fixed. Muris proposes the correlated interaction term estimator (CITE), demonstrating its consistency under economically plausible assumptions that relax traditional restrictions. This contribution provides practitioners with a robust tool for analyzing heterogeneous treatment effects and moderating relationships in longitudinal data. While his citation count is still building, the methodological rigor of his work positions him as a rising voice in panel data econometrics. His research speaks directly to applied researchers in labor, health, and development economics who rely on panel data to uncover nuanced causal relationships.