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Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Arxiv
0+阅读 · 2月19日
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
Arxiv
0+阅读 · 2月17日
Difference-in-Differences in the Presence of Unknown Interference
Arxiv
0+阅读 · 2月17日
Causal Effect Estimation with Latent Textual Treatments
Arxiv
0+阅读 · 2月17日
Measures for Assessing Causal Effect Heterogeneity Unexplained by Covariates
Arxiv
0+阅读 · 2月9日
Distributional Balancing for Causal Inference: A Unified Framework via Characteristic Function Distance
Arxiv
0+阅读 · 2月5日
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
Arxiv
0+阅读 · 2月13日
Boundary Discontinuity Designs: Theory and Practice
Arxiv
0+阅读 · 2月13日
Bounding Treatment Effects by Pooling Limited Information across Observations
Arxiv
0+阅读 · 2月9日
Multivariate Causal Effects: a Bayesian Causal Regression Factor Model
Arxiv
0+阅读 · 2月8日
Identification and Debiased Learning of Causal Effects with General Instrumental Variables
Arxiv
0+阅读 · 2月7日
Triply Robust Panel Estimators
Arxiv
0+阅读 · 2月9日
Estimating causal effects of functional treatments with modified functional treatment policies
Arxiv
0+阅读 · 2月9日
Unraveling time-varying causal effects of multiple exposures: integrating Functional Data Analysis with Multivariable Mendelian Randomization
Arxiv
0+阅读 · 2月16日
Causal Effect Estimation with Learned Instrument Representations
Arxiv
0+阅读 · 2月10日
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