Estimating Potential Outcome Distributions with Collaborating Causal Networks
This work addresses a limitation in causal inference for researchers and practitioners by enabling more detailed outcome analysis without restrictive assumptions, though it appears incremental as it builds on existing distribution estimation methods.
The paper tackles the problem of estimating full potential outcome distributions in causal inference, which provides more comprehensive insights than traditional Conditional Average Treatment Effect (CATE) methods, and demonstrates that CCN learns improved distribution estimates and decisions compared to existing methods in synthetic and semi-synthetic experiments.
Traditional causal inference approaches leverage observational study data to estimate the difference in observed and unobserved outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the full potential outcome distributions. Estimation of outcome distributions via the CCN framework does not require restrictive assumptions of the underlying data generating process. Additionally, CCN facilitates estimation of the utility of each possible treatment and permits individual-specific variation through utility functions. CCN not only extends outcome estimation beyond traditional risk difference, but also enables a more comprehensive decision-making process through definition of flexible comparisons. Under assumptions commonly made in the causal literature, we show that CCN learns distributions that asymptotically capture the true potential outcome distributions. Furthermore, we propose an adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational data. Finally, we evaluate the performance of CCN in multiple synthetic and semi-synthetic experiments. We demonstrate that CCN learns improved distribution estimates compared to existing Bayesian and deep generative methods as well as improved decisions with respects to a variety of utility functions.