| Presentation | 2022-03-08 Estimating average causal effect of intervention in continuous variables using machine learning Yoshiaki Kitazawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The most widely discussed methods for estimating the Average Causal Effect / Average Treatment Effect are those for intervention in discrete binary variables whose value represents the intervention / non-intervention groups. On other hands, methods for intervening in a continuous variables independent of the data generating model has not been developed. In this study, I give a method for estimating the average causal effect for intervention in continuous variables that can be applied to data of any generating model as long as the causal effect is identifiable. The proposed method is independent of machine learning algorithms and preserves the identifiability of the data. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | causal effect / causal effect identifiability / average causal effect / average treatment effect / meta-leaner |
| Paper # | IBISML2021-30 |
| Date of Issue | 2022-03-01 (IBISML) |
| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2022/3/8(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning, etc. |
| Chair | Ichiro Takeuchi(Nagoya Inst. of Tech.) |
| Vice Chair | Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Masashi Sugiyama(Univ. of Tokyo) |
| Assistant | Tomoharu Iwata(NTT) / Atsuyoshi Nakamura(Hokkaido Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Estimating average causal effect of intervention in continuous variables using machine learning |
| Sub Title (in English) | |
| Keyword(1) | causal effect |
| Keyword(2) | causal effect identifiability |
| Keyword(3) | average causal effect |
| Keyword(4) | average treatment effect |
| Keyword(5) | meta-leaner |
| Keyword(6) | |
| Keyword(7) | |
| 1st Author's Name | Yoshiaki Kitazawa |
| 1st Author's Affiliation | NTT DATA Mathematical Systems Inc.(MSI) |
| Date | 2022-03-08 |
| Paper # | IBISML2021-30 |
| Volume (vol) | vol.121 |
| Number (no) | IBISML-419 |
| Page | pp.pp.1-7(IBISML), |
| #Pages | 7 |
| Date of Issue | 2022-03-01 (IBISML) |