1 citations · 1 across the 26 of their papers we have counts for
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Multi-armed Bandits with Missing Outcome
Ilia Mahrooghi, Mahshad Moradi, Sina Akbari +1
While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perha…
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar Kiyavash
Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learn…
Optimal Local Convergence Rates of Stochastic First-Order Methods under Local -PL
Saeed Masiha, Saber Salehkaleybar, Niao He +2
We study the local convergence rate of stochastic first-order methods under a local -Polyak-Lojasiewicz (-PL) condition in a neighborhood of a target connected component $\ma…
Fast Proxy Experiment Design for Causal Effect Identification
Sepehr Elahi, Sina Akbari, Jalal Etesami +2
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (ran…
Causal Effect Identification in LiNGAM Models with Latent Confounders
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar +2
We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the…
Causal Effect Identification in a Sub-Population with Latent Variables
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash +1
The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem…
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…
Confounded Budgeted Causal Bandits
Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
We study the problem of learning 'good' interventions in a stochastic environment modeled by its underlying causal graph. Good interventions refer to interventions that maximize re…