Differentially Private Individual Treatment Effect Estimation from Aggregated Data

Individual Treatment Effect (ITE) estimation has become one of the main trends in Causal Inference due to its applications in various areas where personalization is key. In order to circumvent the complex problem of causal identification, the randomized control trial (RCT) setup is used in several domains which refer to ITE estimation as uplift modeling. If practitioners used to have full access to the user-level data in order to learn uplift models, the rise of privacy concerns in different domains such as healthcare or online advertising motivates to explore how such models could be trained to reach significant performances while ensuring relevant privacy guarantees. We present-ADUM, an-differentially private method to learn uplift models from data aggregated according to a given partition of the feature space. After adapting the bias-variance decomposition to the Precision in Estimation of Heterogeneous Effects (PEHE) metric, we propose an upper bound of the performance of-ADUM under a set of illustrative assumptions, which explicits the privacy-utility trade-off for this class of models and provides insights on how the size of the underlying partition can be adapted to match the privacy constraints. Finally, we provide experiments on both synthetic and real data highlighting that-ADUM outperforms-differentially private models with access to individual data for strong privacy guarantees (≤ 5).