Estimating direct effects of time-varying factors like childhood wealth on political participation is challenging due to complex confounding. Traditional regression methods face a bias trade-off when baseline treatments affect both later outcomes and their associated variables.
Using epidemiological tools, this paper introduces an alternative approach: Marginal Structural Modeling (MSM). We show how MSMs can generate unbiased estimates of controlled direct effects by addressing post-treatment biases effectively.
Data & Methods:
* Childhood wealth data collected through retrospective surveys and birth records.
* Longitudinal tracking of individuals' political participation across adult years.
* Implementation details for overcoming bias in MSM estimation.
Key Findings:
* MSMs significantly reduce estimation bias compared to traditional regression methods.
* The method is particularly valuable when treatments have multiple categories or outcomes are non-continuous.
* Examining the effect of childhood wealth through an MSM reveals important nuances missed by earlier approaches.
Why It Matters:
* Improves understanding and application of causal inference in political science research.
* Provides a robust tool for analyzing time-varying factors even when later treatment variables exist.
| Estimating Controlled Direct Effects through Marginal Structural Models was authored by Michelle Torres. It was published by Cambridge in PSR&M in 2020. |
