activity
20172025
most citedCausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

73 citations · 80 across the 7 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

stat.ML2023

Approximate Causal Effect Identification under Weak Confounding

Ziwei Jiang, Lai Wei, Murat Kocaoglu

Causal effect estimation has been studied by many researchers when only observational data is available. Sound and complete algorithms have been developed for pointwise estimation…

cs.LG2023

Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge

Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu

Causal effect estimation from data typically requires assumptions about the cause-effect relations either explicitly in the form of a causal graph structure within the Pearlian fra…

cs.LG2023

Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models

Zeyu Zhou, Ruqi Bai, Sean Kulinski +2

Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when the causal variables are unobserved and the obs…

cs.IT20233 cited

Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier

Spencer Compton, Dmitriy Katz, Benjamin Qi +2

Given a set of discrete probability distributions, the minimum entropy coupling is the minimum entropy joint distribution that has the input distributions as its marginals. This ha…

cs.AI2023

Characterization and Learning of Causal Graphs with Small Conditioning Sets

Murat Kocaoglu

Constraint-based causal discovery algorithms learn part of the causal graph structure by systematically testing conditional independences observed in the data. These algorithms, su…