Summary of Identification and Estimation Of Conditional Average Partial Causal Effects Via Instrumental Variable, by Yuta Kawakami et al.
Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
by Yuta Kawakami, Manabu Kuroki, Jin Tian
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to estimating heterogeneous causal effects in settings with continuous treatments. The authors focus on conditional average partial causal effects (CAPCE), which reveal the heterogeneity of causal effects. They provide conditions for identifying CAPCE in instrumental variable settings, showing that CAPCE is identifiable under weaker assumptions than traditional methods. Three families of CAPCE estimators are developed: sieve, parametric, and reproducing kernel Hilbert space (RKHS)-based, with their statistical properties analyzed. The proposed estimators are illustrated on synthetic and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how different people might react differently to the same treatment. It looks at a special type of effect called CAPCE, which shows us this difference. To do this, it uses something called instrumental variables. What’s cool is that CAPCE can be identified with weaker assumptions than other methods. The authors also develop three ways to estimate CAPCE: using sieves, being very specific about the parameters, and using a special type of math called RKHS. They test these methods on made-up data and real-world examples. |
