New!
Our paper titled “Characterization and Learning of Causal Graphs with
Latent Variables from Soft Interventions” is accepted at NeurIPS’19.

New!
Our paper titled “Sample Efficient Active Learning of Causal Trees” is
accepted at NeurIPS’19.

I gave a talk on the Shannon Channel on entropic methods
for causal inference, you can watch it here.

Our paper titled “Experimental Design for Cost-Aware
Learning of Causal Graphs” is accepted at NeurIPS’18.

Our paper titled “CausalGAN: Learning Causal Implicit
Generative Models with Adversarial Training” is accepted at ICLR’18. A preprint
is available here.

Our paper titled “Experimental Design for Learning Causal
Graphs with Latent Variables” is accepted at NIPS’17. A preprint is available here.

A new preprint titled “CausalGAN: Learning Causal
Implicit Generative Models with Adversarial Training” is available here.
Tensorflow implementation is available here.

Our paper titled “Cost-Optimal Learning of Causal Graphs”
is accepted at ICML’17. A preprint is available here.

Our paper titled “Entropic Causality and Greedy Minimum
Entropy Coupling” is accepted at ISIT 2017. A preprint is available here.

A new preprint titled “Sparse Quadratic Logistic
Regression in Sub-quadratic Time” is available here.

Our
paper titled "Contextual Bandits with Latent Confounders: An NMF
Approach" is accepted at AISTATS 2017. A preprint is available here.

Python code is available for the entropy-based
causal inference algorithm of "Entropic Causal Inference" paper here.

Our paper titled "Entropic Causal Inference" is accepted at AAAI 2017. A preprint is available here.

Our paper titled "Learning Causal Graphs with Constraints" is accepted for a poster presentation in the NIPS 2016 workshop What if? Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems.

Our paper titled "Sparse Polynomial Learning and Graph Sketching" is accepted for oral presentation at NIPS 2014.