9 citations · 18 across the 8 of their papers we have counts for
8 papers
Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity
David Strieder, Mathias Drton
A fundamental challenge of scientific research is inferring causal relations based on observed data. One commonly used approach involves utilizing structural causal models that pos…
Dual Likelihood for Causal Inference under Structure Uncertainty
David Strieder, Mathias Drton
Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postu…
Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances
David Strieder, Mathias Drton
Inferring the effect of interventions within complex systems is a fundamental problem of statistics. A widely studied approach employs structural causal models that postulate noisy…
Discussion of "A note on universal inference" by Timmy Tse and Anthony Davison
Mathias Drton, Hongjian Shi, David Strieder
Invited discussion for Stat of "A note on universal inference" by Timmy Tse and Anthony Davison (2022)
Rank-Based Causal Discovery for Post-Nonlinear Models
Grigor Keropyan, David Strieder, Mathias Drton
Learning causal relationships from empirical observations is a central task in scientific research. A common method is to employ structural causal models that postulate noisy funct…
On the choice of the splitting ratio for the split likelihood ratio test
David Strieder, Mathias Drton
The recently introduced framework of universal inference provides a new approach to constructing hypothesis tests and confidence regions that are valid in finite samples and do not…