42 citations · 197 across the 45 of their papers we have counts for
51 papers
Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variables
Ana Leticia Garcez Vicente, Saber Salehkaleybar
Continuous-time stochastic differential equations (SDEs) provide a natural framework for modeling interacting dynamical systems. A central causal question is whether causal effects…
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…
One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State
Saber Salehkaleybar
We study the problem of recovering the parameters of a multivariate Ornstein-Uhlenbeck (OU) process from steady-state observational and interventional data. In many applications, s…
The Role of Causality in Algorithmic Recourse
Srikanth Avasarala, Varun Gupta, Shahin Jabbari +2
Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage appli…
Inference Time Causal Probing in LLMs
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1
Causal probing methods aim to test and control how internal representations influence the behavior of generative models. In causal probing, an intervention modifies hidden states s…
Data-Driven and Cycle-Agnostic Covariate Selection for Nonparametric Causal Effect Estimation
Ana Leticia Garcez Vicente, Gijs van Seeventer, Saber Salehkaleybar
Estimating causal effects from observational data requires identifying valid adjustment sets. This task is especially challenging in realistic settings where latent confounding and…
Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems
Gijs van Seeventer, Saber Salehkaleybar
We study identifiability in continuous-time linear stationary stochastic differential equations with a known causal structure. Unlike existing approaches, we relax the assumption o…
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Lincen Yang, Zhong Li, Matthijs van Leeuwen +1
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While m…
Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
Ehsan Sharifian, Saber Salehkaleybar, Negar Kiyavash
We study the problem of causal structure learning from a combination of observational and interventional data generated by a linear non-Gaussian structural equation model that migh…
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…
Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery
Kacper Kadziolka, Saber Salehkaleybar
Causal inference remains a fundamental challenge for large language models. Recent advances in internal reasoning with large language models have sparked interest in whether state-…
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…
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar +1
We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder. We first show that the causal effect is identifiabl…
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar +2
This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups…
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…
ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders
Andreas Sauter, Saber Salehkaleybar, Frank van Harmelen +2
Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challenging due to causal ambiguity, rest…
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…
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…
Soft Preference Optimization: Aligning Language Models to Expert Distributions
Arsalan Sharifnassab, Saber Salehkaleybar, Sina Ghiassian +2
We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward…
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Arsalan Sharifnassab, Saber Salehkaleybar, Richard Sutton
We address the challenge of optimizing meta-parameters (hyperparameters) in machine learning, a key factor for efficient training and high model performance. Rather than relying on…
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…
Efficiently Escaping Saddle Points for Policy Optimization
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +2
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance. In recent years, several variance-reduced PG methods have been proposed w…
A Cross-Moment Approach for Causal Effect Estimation
Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash
We consider the problem of estimating the causal effect of a treatment on an outcome in linear structural causal models (SCM) with latent confounders when we have access to a singl…
Fast Causal Orientation Learning in Directed Acyclic Graphs
Ramin Safaeian, Saber Salehkaleybar, Mahmoud Tabandeh
Causal relationships among a set of variables are commonly represented by a directed acyclic graph. The orientations of some edges in the causal DAG can be discovered from observat…