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The Future Strikes Back: Using Future Treatments to Detect and Reduce Hidden Bias
Felix Elwert1, Fabian T Pfeffer2
1University of Wisconsin-Madison, WI, USA.
Sociological Methods & Research
|August 26, 2022
Summary
This study demonstrates that using future treatment values in regression analysis can effectively mitigate omitted variable bias. This novel approach aids in detecting and reducing unobserved confounding for more accurate causal inference.
Area of Science:
- Econometrics
- Causal Inference
- Statistical Modeling
Background:
- Conventional regression analysis often advises against controlling for postoutcome variables.
- Omitted variable bias (unobserved confounding) remains a significant challenge in causal inference.
Purpose of the Study:
- To investigate the utility of controlling for future treatment values to address omitted variable bias.
- To introduce and evaluate new methods for bias reduction and detection using future treatment information.
Main Methods:
- Developed a new approach to control for future treatment values in regression analysis.
- Introduced a nonparametric test to detect hidden bias using future treatments.
- Empirically illustrated the methods with data on parental income and children's educational attainment.
Main Results:
- The proposed method strictly reduces omitted variable bias.
- Existing approaches may inadvertently increase bias.
- A new test effectively detects hidden bias, even when estimation methods fail.
Conclusions:
- Controlling for future treatment values is a productive strategy to combat omitted variable bias.
- The developed methods offer advancements in detecting and reducing unobserved confounding in observational studies.
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