A Framework for Dynamic Causal Inference in Political Science

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How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It (2018, background): “More recent work has amplified these points in the context of observational research (Blackwell 2013; Acharya, Blackwell, and Sen 2016).”
Robust Estimation of Inverse Probability Weights for Marginal Structural Models (2015, methodology): “, Blackwell 2013). As explained by Robins, Hernán, and Brumback (2000), when estimating the causal effects of time-varying treatments, standard regression models fail to appropriately adjust for time-dependent observed confounders that are affected by previous treatments.”
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? (2019, background): “Since fixed effects models can only adjust for unobserved confounders that are time-invariant, applied researchers often measure a vector of observed time-varying confounders (10)See Blackwell (2013) and Blackwell and Glynn (2018) for recent articles that introduce relevant modeling techniques under”
Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects (2016, background): “See replication materials for more details.”