2 citations · 2 across the 5 of their papers we have counts for
5 papers
Recursive Causal Discovery
Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari +1
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scien…
Non-linear Triple Changes Estimator for Targeted Policies
Sina Akbari, Negar Kiyavash
The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications. To address this issue, the…
Causal Imitability Under Context-Specific Independence Relations
Fateme Jamshidi, Sina Akbari, Negar Kiyavash
Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and t…
Learning Causal Graphs via Monotone Triangular Transport Maps
Sina Akbari, Luca Ganassali, Negar Kiyavash
We study the problem of causal structure learning from data using optimal transport (OT). Specifically, we first provide a constraint-based method which builds upon lower-triangula…
Learning Bayesian Networks in the Presence of Structural Side Information
Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi +2
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 st…