Selects, audits, and explains causal machine learning workflows for heterogeneous treatment effects and graph-based causal ML. Use for CATE, ITE, uplift modeling, S/T/X/R/DR learners, causal forests, matching, propensity scores, IV/DRIV, CEVAE/DragonNet,…
Langue du texte source : anglais