9 citations · 15 across the 7 of their papers we have counts for
7 papers
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…
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…
On Identifiability of Conditional Causal Effects
Yaroslav Kivva, Jalal Etesami, Negar Kiyavash
We address the problem of identifiability of an arbitrary conditional causal effect given both the causal graph and a set of any observational and/or interventional distributions o…
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…
Revisiting the General Identifiability Problem
Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami +1
We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of obs…
(Dis)assortative Partitions on Random Regular Graphs
Freya Behrens, Gabriel Arpino, Yaroslav Kivva +1
We study the problem of assortative and disassortative partitions on random -regular graphs. Nodes in the graph are partitioned into two non-empty groups. In the assortative par…