Understanding causal relations is vital in scientific discovery. The process of causal structure
learning involves identifying causal graphs from observational data to understand such …

Recently, several methods such as private ANM, EM-PC and Priv-PC have been proposed
to perform differentially private causal discovery in various scenarios including bivariate …

Patient privacy is a major barrier to healthcare AI. For confidentiality reasons, most patient
data remains in silo in separate hospitals, preventing the design of data-driven healthcare AI …

Local differential privacy (LDP) is a differential privacy (DP) paradigm in which individuals
first apply a DP mechanism to their data (often by adding noise) before transmitting the result …

Merging datasets across institutions is a lengthy and costly procedure, especially when it
involves private information. Data hosts may therefore want to prospectively gauge which …

Adaptive experiment is widely adopted to estimate conditional average treatment effect
(CATE) in clinical trials and many other scenarios. While the primary goal in experiment is to …

Data-driven targeted interventions have become a powerful tool for organizations to
optimize business outcomes by utilizing individual-level data from experiments. A key …

Experiment design has a rich history dating back over a century and has found many critical
applications across various fields since then. The use and collection of users' data in …

Estimating causal effects from randomized experiments is only feasible if participants agree
to reveal their potentially sensitive responses. Of the many ways of ensuring privacy, label …

Background: Artificial intelligence (AI) is a priority for tech companies today. Considering its
perceived value and power, people are paying attention to both the promise and the peril of …