- The paper introduces a novel method that derives sharp bounds on the probability of causation despite sample selection challenges.
- It employs a three-tier framework—exogeneity with monotone selection, monotone treatment response, and stochastic dominance—to enhance causal inference.
- Empirical results from the Jóvenes en Acción program show a 10.2% to 13.4% effect on employment formality, though the impact remains statistically ambiguous.
Identifying the Probability of Causation in the Presence of Sample Selection
The paper "Probability of Causation with Sample Selection: A Reanalysis of the Impacts of Jóvenes en Acción on Formality" addresses the challenge of estimating the probability of causation in datasets where sample selection occurs. This is a critical issue as numerous policy evaluation scenarios require accurate causal inference in the presence of such selection biases.
Key Contributions and Methodology
The authors tackle the estimation problem by deriving sharp bounds on the probability of causation. These bounds are applicable to individuals who consistently appear in the sample, regardless of treatment status, under three progressively restrictive assumption sets:
- Exogeneity and Monotone Sample Selection: Assumes the treatment is exogenous and imposes that the sample selection process is monotonically non-decreasing with respect to treatment.
- Monotone Treatment Response: Adds the assumption that treatment has a non-decreasing effect on the potential outcomes.
- Stochastic Dominance: Assumes the subpopulation that self-selects into the sample irrespective of treatment has higher treated potential outcomes than those who enter the sample only when treated.
These methodologies are rigorously developed using a potential outcomes framework and ensure partial identification of causal parameters even when considering endogenous selection processes. The bounds they propose offer robustness to different data-generating processes, drawing from theoretical underpinnings in causal inference, nonparametric bounds, and sample selection models.
Empirical Illustration and Results
The theoretical framework is applied to data from the Colombian job training program, "Jóvenes en Acción", which provides a practical illustration of the utility of this approach. Despite using an intensive training intervention, the results suggest that, among always-employed women, participation in the program led to formality in employment for at least 10.2% and at most 13.4%. However, the confidence regions established do not reject the null hypothesis that the true impact could be zero, thus highlighting the potential limitations of program effectiveness in certain subgroups.
Implications and Future Directions
The methodological advances in this paper have significant implications for both the evaluation of social programs and broader applications in fields where selection bias and causality are concerns. The approach provides a valuable tool for robust causal inference without heavily relying on potentially restrictive parametric assumptions.
The authors indicate several promising avenues for future research. Extending these methods to handle more complex or generalized forms of treatment effects can provide broader utility in various settings. Additionally, integrating this approach with machine learning techniques could improve prediction capabilities when dealing with high-dimensional confounders and treatment heterogeneity.
Overall, this study represents a significant step towards tackling complex identification problems in causal inference, offering practical solutions to a longstanding issue experienced in many empirical research contexts.