See my most recent publications on my Google Scholar page.

Journal publications:

[JMLR, 2025] ‘Optimal Experiment Design for Causal Effect Identification’ Journal of Machine Learning Research (JMLR) special issue for ICML&NeurIPS outstanding papers.

[JMLR, 2025] ‘Recursive Causal Discovery’

[TMLR, 2023] ‘A Free Lunch with Influence Functions? An Empirical Eval- uation of Influence Functions for Average Treatment Effect Estimation’

[JMLR, 2021] ‘A Recursive Markov Boundary-Based Approach to Causal Structure Learning’

Conference publications:

[UAI, 2025] ‘Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments’

[UAI, 2025] ‘Multi-armed Bandits with Missing Outcomes’

[CLeaR, 2025] ‘Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding’

[NeurIPS, 2024] ‘Fast Proxy Experiment Design for Causal Effect Identification’ to appear at NeurIPS 2024.

[ICML, 2024, SPOTLIGHT] ‘Triple changes estimator for targeted policies’

[NeurIPs, 2023] ‘Causal effect identification in uncertain causal networks’

[NeurIPS, 2023] ‘Causal imitability under context-specific independence relations’

[ICML, 2022, ORAL – Outstanding paper runner up award] ‘Minimm-cost Intervention Design for Causal Effect Identification’.

[AAAI, 2022] ‘Learning Bayesian Networks in the Presence of Structural Side Information’

[NeurIPS, 2021] ‘Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias’.

Workshop Publications:

[NeurIPS, 2023 – workshop on Optimal Transport and Machine Learning (OTML)] ‘Causal Discovery via Monotone Triangular Transport Maps’

Preprints:

‘Semiparametric Triple Difference Estimators’

‘CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers and Fully-Connected Neural Networks for Causally Constrained Predictions’