322 citations · 358 across the 40 of their papers we have counts for
10 papers · 1 filter
Path-specific harm decomposition: A partial identification framework
Ruizi Yan, Dennis Frauen, Maresa Schröder +1
A central goal when designing treatment policies is often to "do no harm", that is, to avoid interventions that improve average outcomes while worsening outcomes for some individua…
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…
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…
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…
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…
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…
Causal Machine Learning for Cost-Effective Allocation of Development Aid
Milan Kuzmanovic, Dennis Frauen, Tobias Hatt +1
The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by 'leaving no one behind', and, to achieve the SDGs by 2030, poor countries r…
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…
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Counterfactual inference aims to answer retrospective "what if" questions and thus belongs to the most fine-grained type of inference in Pearl's causality ladder. Existing methods…
Estimating average causal effects from patient trajectories
Dennis Frauen, Tobias Hatt, Valentyn Melnychuk +1
In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controll…