3 papers
cs.LG2026
CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
Huiyang Yi, Xiaojian Shen, Yonggang Wu +3
Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on untestable causal assumptions and by the…
stat.ME2024
Causal Inference under Network Interference Using a Mixture of Randomized Experiments
Yiming Jiang, He Wang
In randomized experiments, the classic Stable Unit Treatment Value Assumption (SUTVA) posits that the outcome for one experimental unit is unaffected by the treatment assignments o…
stat.AP2024
Causal Inference in Social Platforms Under Approximate Interference Networks
Yiming Jiang, Lu Deng, Yong Wang +1
Estimating the total treatment effect (TTE) of a new feature in social platforms is crucial for understanding its impact on user behavior. However, the presence of network interfer…