אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Causal inference is increasingly used in high-stakes domains to support decision-making and policy design. However, causal conclusions may be sensitive to the composition of the observed data, raising an important question: could a small number of additional, plausible observations substantially change the estimated causal effect? Answering this question is essential for assessing the robustness of causal conclusions and identifying underrepresented subpopulations whose inclusion could meaningfully affect the estimates, thereby guiding future data collection. In this work, we introduce the problem of causal effect steering via synthetic data augmentation: given a user-defined target range, identify a minimal-size set of realistic synthetic tuples whose addition shifts the estimated effect into that range. By searching for small augmentations that induce substantial changes in the estimated effect, this approach provides a systematic way to probe the sensitivity of causal conclusions to plausible changes in the observed population. To this end, we present CURATE, a framework for causal sensitivity analysis through synthetic data augmentation. CURATE leverages recent advances in synthetic tabular data generation and adapts a top-performing diffusion model to generate realistic candidate tuples. It then employs a heuristic search method to identify small augmentations that steer the estimated causal effect toward the target range. Experiments on six real-world datasets demonstrate that CURATE efficiently identifies small, realistic data augmentations that reveal the robustness of causal conclusions and expose influential underrepresented populations.