7 citations · 24 across the 16 of their papers we have counts for
4 papers · 1 filter
Causal Discovery in Semi-Stationary Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas…
Causal Discovery-Driven Change Point Detection in Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Change point detection in time series aims to identify moments when the probability distribution of time series changes. It is widely applied in many areas, such as human activity…
Limits of Approximating the Median Treatment Effect
Raghavendra Addanki, Siddharth Bhandari
Average Treatment Effect (ATE) estimation is a well-studied problem in causal inference. However, it does not necessarily capture the heterogeneity in the data, and several approac…
Continuous Treatment Effects with Surrogate Outcomes
Zhenghao Zeng, David Arbour, Avi Feller +4
In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missin…