Research map / Probabilistic and scientific modeling / Treatment effects
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the estimated regime's recommended doses instead of the original doses | Proposed method · Ohio type 1 diabetes, test data | average lag 4 weighted advantage: 0.13 to 0.63 | Helped |
| Estimate the optimal regime with Sₜ=Xₜ despite θ₂=-0.1 violating the required assumption for lag 1 | Proposed method · Simulation, lag 3 weighted advantage | weighted advantage: 64.5×10⁻³; 63.9×10⁻³, 64.0×10⁻³ and 64.1×10⁻³ | No clear effect |
| Increase simulation sample size | Proposed method · Simulation, lag 3 weighted advantage | weighted advantage: 64.7 (0.27); 64.8 (0.13); 64.9 (0.06) | No clear effect |
Closest papers by meaning
- Causal Identification for Complex Functional Longitudinal Studies (ICLR 2025 · similarity 0.85)
- Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments (ICML 2024 · similarity 0.85)
- Incremental Causal Effect for Time to Treatment Initialization (ICLR 2025 · similarity 0.84)
- Causal Attribution Analysis for Continuous Outcomes (ICML 2025 · similarity 0.83)
- A Unified Framework for the Transportability of Population-Level Causal Measures (NeurIPS 2025 · similarity 0.83)
- End-To-End Causal Effect Estimation from Unstructured Natural Language Data (NeurIPS 2024 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.