JSAI2024

Presentation information

General Session

General Session » GS-2 Machine learning

[2D5-GS-2] Machine learning: Statistical learning

Wed. May 29, 2024 3:30 PM - 5:10 PM Room D (Temporary room 2)

座長:高橋 大志(日本電信電話株式会社)

4:30 PM - 4:50 PM

[2D5-GS-2-04] Adversarial Nuisance for Causal Effect Estimation

〇Akria Tanimoto1 (1. NEC)

Keywords:Causal inference, Pessimistic inference

Causal inference is the problem of inferring the outcome of decisions that may differ from those taken in the past. One of the basic approaches is importance sampling, in which sample weights are generally the inverse of the propensity score, a nuisance parameter estimated a priori and plugged-in. A problem here is the estimated variance especially when the propensity score can be extremely small. This is particularly problematic when the randomness of the past decision is low or when the hypothesis space is complex, such as with deep models or high-dimensional data. We propose an end-to-end estimation method based on a generalized error upper bound by taking the worst-case for the uncertainty in the nuisance estimation.

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