Research map / Probabilistic and scientific modeling / Treatment effects
A Bayesian Approach for Estimating Causal Effects from Observational Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Bayesian model averaging over DAGs with Bayesian regression instead of IDA variants | BIDA · Simulated 20-node DAGs, n=50,200,800 | MSE of causal effect | Helped |
| increase sample size | IDAmap and IDAoracle vs BIDA · Simulated 20-node DAGs | MSE | No clear effect |
| use ancestor relation probabilities (ARP) instead of BIDA for non-zero effects | causal relation discovery · Simulated, any non-zero effect | precision-recall AUC | Helped |
| use BIDA instead of ARP for strong effects (|theta|>=0.2) | causal relation discovery · Simulated, strong effects | precision-recall AUC | Mixed |
| average over DAGs (BMA) instead of a single best structure (ablation) | BIDA · Simulated 20-node DAGs | MSE | Helped |
Closest papers by meaning
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- A Meta-Learning Approach to Bayesian Causal Discovery (ICLR 2025 · similarity 0.88)
- CausalPFN: Amortized Causal Effect Estimation via In-Context Learning (NeurIPS 2025 · similarity 0.88)
- Causal Attribution Analysis for Continuous Outcomes (ICML 2025 · similarity 0.88)
- Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning (NeurIPS 2025 · similarity 0.88)
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