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Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use causal diagram to reduce policy space (Reduce algorithm, minimal reduction) before online learning | OFU-DTR / PS-DTR · Lung Cancer and Dyspnoea SCMs | cumulative regret | Helped |
| Add causal bounds from confounded observational data as warm start for OFU-DTR (ablation) | OFU-DTR · Lung Cancer | cumulative regret | Helped |
| Add causal bounds from observational data to PS-DTR via rejection sampling (ablation) | PS-DTR · Lung Cancer | convergence to optimal policy | Helped |
| Use posterior sampling (PS-DTR) instead of optimistic planning (OFU-DTR) in pure online setting (ablation) | PS-DTR · Lung Cancer | convergence speed | Helped |
| Use PS-DTR instead of OFU-DTR in pure online setting (ablation) | PS-DTR · Dyspnoea | cumulative regret | Helped |
| Use PS-DTR with causal bounds instead of OFU-DTR with causal bounds (ablation) | PS-DTR+ · Dyspnoea | cumulative regret | No clear effect |
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