밤에 쓰는 편지 밤에 쓰는 편지

* Introduction to Causal Inferencehttps://www.bradyneal.com/causal-inference-course#course-textbookhttps://www.youtube.com/playlist?list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0 * A First Course in Causal Inferencehttps://arxiv.org/pdf/2305.18793https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZX3VEV * Causal Inference What If https://static1.squarespace.com/static/675db8b0dd37..

https://arxiv.org/pdf/1705.08821https://github.com/AMLab-Amsterdam/CEVAENeurIPS 2017AbstractLearning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, fact..

https://arxiv.org/pdf/2107.10483https://github.com/phlippe/ENCOICLR 2022 https://www.youtube.com/watch?v=ro_5FqiS5qUAbstract Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields. A promising direction is continuous optimization for score-based methods, which, however, require constrained optimizatio..

https://arxiv.org/abs/2101.10943https://github.com/AliciaCurth/CATENets(AISTATS 2021) https://www.youtube.com/watch?v=CkQCwh50SEk★ 동영상을 꼭 봐야한다!! 논문에서 설명하지 않아서 이해하지 못했던 개념들을 명확하게 설명해주신다. ★e.g. pseudo-outcomes, etc.AbstractThe need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly. To do so..