Synthesizing the Counterfactual: A CTGAN-Augmented Causal Evaluation of Palliative Care on Spousal Depression

2026-03-27

AI Summary

This study addresses the causal evaluation of palliative care’s impact on spousal depression, a task hindered by small sample sizes and high attrition in longitudinal dyadic data. To overcome these limitations, the authors innovatively employ a Conditional Tabular Generative Adversarial Network (CTGAN) to synthesize sparse treated-group observations, integrating this with a matched difference-in-differences design, Oster’s robustness test for unobserved confounding, and baseline-constrained synthetic trajectory anchoring to preserve causal pathways while strengthening quasi-experimental validity. The findings reveal a nonlinear dynamic effect: palliative care initially exacerbates depressive symptoms (β₀ = 0.218, p < 0.05) but subsequently promotes significant psychological recovery (β₂ = −0.763, p < 0.01). These results demonstrate high robustness to potential unobserved confounders.

Abstract

Spousal bereavement severely deteriorates mental health. While palliative care benefits dying patients, its "stress-buffering" effect on survivors' depression remains empirically elusive due to acute small-$N$ constraints in longitudinal dyadic data. This study evaluates the causal impact of palliative care on bereaved spouses while introducing Synthetic Data Generation (SDG) to resolve sample attrition in quasi-experimental designs. Using SHARE panel data, we augment the sparse treated cohort via a Conditional Tabular GAN, anchoring synthetic trajectories to empirical baseline constraints to preserve causal pathways. A Matched Difference-in-Differences estimator applied to the high-fidelity augmented dataset evaluates the treatment effect. Results reveal a non-linear psychological response. Palliative care initially exacerbates acute depressive symptoms at the time of loss ($β_0 = 0.218,\ p < 0.05$), reflecting the intense emotional confrontation of the intervention. However, a sustained stress-buffering effect emerges in subsequent periods ($β_2 = -0.763,\ p < 0.01$), indicating an accelerated long-term recovery compared to standard care. Estimates are highly robust to unobserved confounding (Oster's $δ> 1$). Substantively, we advocate for reconceptualizing end-of-life care as a dyadic public health intervention. Methodologically, we establish SDG as a robust analytical tool capable of powering fragile quasi-experiments in longitudinal social surveys.

Problem

• palliative care
• spousal depression
• causal inference
• sample attrition
• longitudinal dyadic data

Innovation

• Synthetic Data Generation
• Conditional Tabular GAN
• Causal Inference
• Matched Difference-in-Differences
• Longitudinal Quasi-Experiment