We propose an adversarial training procedure for learning a causal implicit generative
model for a given causal graph. We show that adversarial training can be used to learn a …

We consider the problem of learning a causal graph over a set of variables with
interventions. We study the cost-optimal causal graph learning problem: For a given …

We consider the problem of identifying the causal direction between two discrete random
variables using observational data. Unlike previous work, we keep the most general …

Entropic causal inference is a framework for inferring the causal direction between two
categorical variables from observational data. The central assumption is that the amount of …

We study the problem of discovering the simplest latent variable that can make two observed
discrete variables conditionally independent. The minimum entropy required for such a …

We study the problem of identifying the causal relationship between two discrete random
variables from observational data. We recently proposed a novel framework called entropie …

Quantum Entropic Causal Inference Page 1 Quantum Entropic Causal Inference Mohammad
Ali Javidian, Vaneet Aggarwal, Fanglin Bao, Zubin Jacob Purdue University, West Lafayette …

It is a task of widespread interest to learn the underlying causal structure for systems of
random variables. Entropic Causal Inference is a recent framework for learning the causal …

We propose different approaches to infer causal influences between agents in a network
using only observed time series. This includes graphical models to depict causal …