AI RESEARCH
Density Ratio-based Proxy Causal Learning Without Density Ratios
arXiv CS.LG
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ArXi:2503.08371v2 Announce Type: replace We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accomplish this task using two proxy variables related to the latent confounder: a treatment proxy (related to the treatment) and an outcome proxy (related to the outcome). Two approaches have been proposed to perform causal effect estimation given proxy variables;. however.