research
∙
06/27/2023
Simulating counterfactuals
Counterfactual inference considers a hypothetical intervention in a para...
share
research
∙
03/29/2023
Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach
Pricing decisions of companies require an understanding of the causal ef...
research
∙
06/14/2022
Generalizing experimental findings: identification beyond adjustments
We aim to generalize the results of a randomized controlled trial (RCT) ...
research
∙
11/30/2021
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Semiparametric inference about average causal effects from observational...
research
∙
11/08/2021
Clustering and Structural Robustness in Causal Diagrams
Graphs are commonly used to represent and visualize causal relations. Fo...
research
∙
09/21/2020
Identifying Causal Effects via Context-specific Independence Relations
Causal effect identification considers whether an interventional probabi...
research
∙
08/31/2020
Simulation Framework for Realistic Large-scale Individual-level Health Data Generation
We propose a general framework for realistic data generation and simulat...
research
∙
07/16/2020
Do-search – a tool for causal inference and study design with multiple data sources
Epidemiological evidence is based on multiple data sources including cli...
research
∙
03/06/2020
Estimation of causal effects with small data under implicit functional constraints
We consider the problem of estimating causal effects of interventions fr...
research
∙
02/04/2019
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach
Causal effect identification considers whether an interventional probabi...
research
∙
06/19/2018
Surrogate Outcomes and Transportability
Identification of causal effects is one of the most fundamental tasks of...
research
∙
06/19/2018
Identifying Causal Effects with the R Package causaleffect
Do-calculus is concerned with estimating the interventional distribution...
research
∙
06/19/2018
Enhancing Identification of Causal Effects by Pruning
Causal models communicate our assumptions about causes and effects in re...
research
∙
06/19/2018
Simplifying Probabilistic Expressions in Causal Inference
Obtaining a non-parametric expression for an interventional distribution...
research
∙
11/16/2017
Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey
Aims: A common objective of epidemiological surveys is to provide popula...
research
∙
03/05/2014
Estimating complex causal effects from incomplete observational data
Despite the major advances taken in causal modeling, causality is still ...
research
∙
11/13/2012