3 citations · 5 across the 4 of their papers we have counts for
5 papers
Counterfactual Optimization of Policy Interventions: Lexical Ordering and Leapfrogging
Martina Scauda, Tobias Freidling, Qingyuan Zhao
Most data-driven policy learning methods maximize average outcomes, overlooking the possibility that a policy beneficial on average may still harm a substantial fraction of individ…
Selective Randomization Inference for Adaptive Experiments
Tobias Freidling, Qingyuan Zhao, Zijun Gao
Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Becaus…
Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations
Tobias Freidling, Qingyuan Zhao
Causal inference necessarily relies upon untestable assumptions; hence, it is crucial to assess the robustness of obtained results to violations of identification assumptions. Howe…
Confidence in Causal Discovery with Linear Causal Models
David Strieder, Tobias Freidling, Stefan Haffner +1
Structural causal models postulate noisy functional relations among a set of interacting variables. The causal structure underlying each such model is naturally represented by a di…
Post-selection inference with HSIC-Lasso
Tobias Freidling, Benjamin Poignard, Héctor Climente-González +1
Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning. Variable selection methods have thus…