Publications by Ehsan Mokhtarian

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We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

Understanding how changes in an environment affect outcomes is at the core of causal inference. Unlike standard prediction tasks that capture associations between variables, causal inference aims to reveal what happens when we intervene in a system. In thi ...

We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show t ...

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring th ...

We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and statistically challengin ...

We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the ...

Ehsan Mokhtarian

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A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Causal Discovery and Causal Effect Identification

Revisiting the General Identifiability Problem

s-ID: Causal Effect Identification in a Sub-Population

Learning Bayesian Networks in the Presence of Structural Side Information

Causal Effect Identification with Context-specific Independence Relations of Control Variables