From the 1 of 83 linked papers with an AI index.
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Targeted Synthetic Control Method
Yuxin Wang, Dennis Frauen, Emil Javurek +3
The synthetic control method (SCM) estimates causal effects in panel data with a single-treated unit by constructing a counterfactual outcome as a weighted combination of untreated…
Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
Emil Javurek, Dennis Frauen, Marie Brockschmidt +2
Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis…
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the…
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal +3
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to…
Generalized Bayes for Causal Inference
Emil Javurek, Dennis Frauen, Yuxin Wang +1
Uncertainty quantification is central to many applications of causal machine learning, yet principled Bayesian inference for causal effects remains challenging. Standard Bayesian a…
DeepBlip: Estimating Conditional Average Treatment Effects Over Time
Haorui Ma, Dennis Frauen, Stefan Feuerriegel
Structural nested mean models (SNMMs) are a principled approach to estimate the treatment effects over time. A particular strength of SNMMs is to break the joint effect of treatmen…