Research map / Learning theory and algorithms / Prediction and decision losses
Sample Efficient Active Learning of Causal Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| intervene on a central node instead of the information-greedy node | adaptive causal tree root search · random trees, n=30, finite samples | average number of interventions | Helped |
| intervene on a central node instead of a node sampled from the prior (random baseline) | adaptive causal tree root search · random trees, n=30 and n=1000 | average number of interventions | Helped |
| increase number of interventional samples per intervention from 1 to 50 | central node algorithm · n=50 | average number of interventions | Helped |
| use information-greedy selection instead of central node | noiseless counterexample tree · constructed counterexample, K=3 | expected interventions: at least K/2 vs ceil(log2 K)+1 | Hurt |
Closest papers by meaning
- Sample Efficient Bayesian Learning of Causal Graphs from Interventions (NeurIPS 2024 · similarity 0.92)
- Adaptive Online Experimental Design for Causal Discovery (ICML 2024 · similarity 0.91)
- Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models (NeurIPS 2025 · similarity 0.89)
- Bayesian Active Learning for Bivariate Causal Discovery (ICML 2025 · similarity 0.89)
- Interventional Causal Discovery in a Mixture of DAGs (NeurIPS 2024 · similarity 0.89)
- Learning Mixtures of Unknown Causal Interventions (NeurIPS 2024 · similarity 0.88)
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