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Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1
We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent…
Active Context Selection Improves Simple Regret in Contextual Bandits
Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash
We study the contextual multi-armed bandit problem with a finite context space (a.k.a. subpopulations), where the learner recommends a best action for each context and is evaluated…
Graph Learning Is Suboptimal in Causal Bandits
Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash
We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying th…
Fusing Rewards and Preferences in Reinforcement Learning
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1
We present Dual-Feedback Actor (DFA), a reinforcement learning algorithm that fuses both individual rewards and pairwise preferences (if available) into a single update rule. DFA u…
Hierarchical Reinforcement Learning with Targeted Causal Interventions
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1
Hierarchical reinforcement learning (HRL) improves the efficiency of long-horizon reinforcement-learning tasks with sparse rewards by decomposing the task into a hierarchy of subgo…
Best Group Identification in Multi-Objective Bandits
Mohammad Shahverdikondori, Mohammad Reza Badri, Negar Kiyavash
We introduce the Best Group Identification problem in a multi-objective multi-armed bandit setting, where an agent interacts with groups of arms with vector-valued rewards. The per…
Multi-Domain Causal Discovery in Bijective Causal Models
Kasra Jalaldoust, Saber Salehkaleybar, Negar Kiyavash
We consider the problem of causal discovery (a.k.a., causal structure learning) in a multi-domain setting. We assume that the causal functions are invariant across the domains, whi…
Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding
Fateme Jamshidi, Sina Akbari, Negar Kiyavash
We study the problem of closeness testing for continuous distributions and its implications for causal discovery. Specifically, we analyze the sample complexity of distinguishing w…
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference
Fateme Jamshidi, Mohammad Shahverdikondori, Negar Kiyavash
We study multi-armed bandits under network interference, where each unit's reward depends on its own treatment and those of its neighbors in a given graph. This induces an exponent…
Pure Exploration Beyond Reward Feedback: The Role of Post-Action Context
Mohammad Shahverdikondori, Amir Mohammad Abouei, Alireza Rezaeimoghadam +1
We introduce the problem of best arm identification (BAI) with post-action context, a new BAI problem in a stochastic multi-armed bandit environment and the fixed-confidence settin…
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…
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 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…
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…
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash
We study the problem of identifying the unknown intervention targets in structural causal models where we have access to heterogeneous data collected from multiple environments. Th…
On sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
Motivated by conditional independence testing, an essential step in constraint-based causal discovery algorithms, we study the nonparametric Von Mises estimator for the entropy of…
s-ID: Causal Effect Identification in a Sub-Population
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
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…