What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
cs.LG
Submitted: 2026-09-29
Updated: 2026-09-29
Code: https://github.com/MLAI-Yonsei/CausalIDView
Sources
- Black Box Causal Inference: Effect Estimation via Meta Prediction
- Computational Identifiability
- TabPFN-3: Technical Report
- Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
- Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017
- TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
- TabPFN-3.5: Technical Report
- Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
- Prior-Data Fitted Networks for Causal Inference: a Simulation Study with Real-World Scenarios
- Transformers Can Do Bayesian Inference
- RealCause: Realistic Causal Inference Benchmarking
- Beyond IID: How General Are Tabular Foundation Models, Really?
- DoWhy: An End-to-End Library for Causal Inference
- DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series
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