10 citations · 17 across the 6 of their papers we have counts for
6 papers
Finding and Listing Front-door Adjustment Sets
Hyunchai Jeong, Jin Tian, Elias Bareinboim
Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such ef…
Causal Effect Identification in Cluster DAGs
Tara V. Anand, Adèle H. Ribeiro, Jin Tian +1
Reasoning about the effect of interventions and counterfactuals is a fundamental task found throughout the data sciences. A collection of principles, algorithms, and tools has been…
Partial Counterfactual Identification from Observational and Experimental Data
Junzhe Zhang, Jin Tian, Elias Bareinboim
This paper investigates the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about…
Supervised Whole DAG Causal Discovery
Hebi Li, Qi Xiao, Jin Tian
We propose to address the task of causal structure learning from data in a supervised manner. Existing work on learning causal directions by supervised learning is restricted to le…
Adjustment Criteria for Recovering Causal Effects from Missing Data
Mojdeh Saadati, Jin Tian
Confounding bias, missing data, and selection bias are three common obstacles to valid causal inference in the data sciences. Covariate adjustment is the most pervasive technique f…
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Hebi Li, Qi Xiao, Shixin Tian +1
Machine learning models are vulnerable to adversarial examples. Iterative adversarial training has shown promising results against strong white-box attacks. However, adversarial tr…