arXiv Statistics ML
Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach
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arXiv Statistics ML
3d ago
Identifying Causal Effects Using a Single Proxy Variable
The paper tackles the problem of estimating causal effects when an unobserved confounder is present. It assumes a single, possibly multi‑dimensional proxy variable for the confounder and knowledge of the mechanism that generates this proxy. Under the Single Proxy Identifiability of Causal Effects (SPICE) assumption, the authors prove that the error mechanism is complete and causal effects are identifiable, extending prior proxy‑based results to continuous, multi‑dimensional settings and more flexible functional forms. They also introduce SPICE‑Net, a neural‑network‑based framework for estimating causal effects applicable to both discrete and continuous treatments.
arXiv AI
Jun 2
Towards a holistic understanding of Selection Bias for Causal Effect Identification
arXiv:2605. 13430v3 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies.
arXiv Machine Learning
Jul 7
Two Layers of Instability in Causal Estimation
arXiv:2606. 21185v2 Announce Type: replace-cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed.
arXiv Statistics ML
Aug 24
Double Machine Learning of Continuous Treatment Effects with Additive Instrumental Variables
The paper introduces a new framework for identifying average dose-response functions in the presence of unmeasured confounding by using instrumental variables. It defines a uniform regular weighting function and partitions the treatment space into open sets where local identification is possible. For estimation, the authors propose an augmented inverse probability weighted score within a debiased machine learning setting, along with practical guidance for constructing weighting functions, falsification tests for the additive IV condition, and asymptotic theory for kernel regression or empirical risk minimization estimators.
arXiv Machine Learning
Sep 10
Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes
arXiv:2609.06294v1 Announce Type: new Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
arXiv Machine Learning
Jul 28
TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure
arXiv:2607. 22762v1 Announce Type: cross Abstract: Causal inference has become a central issue across various fields, including computer science, statistics, economics, education, healthcare, and medicine.