42 citations · 125 across the 38 of their papers we have counts for
48 papers
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 Covariate Selection for Nonparametric and Cycle-Agnostic 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…