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Found 11 papers, 3 papers with code
Simulating counterfactuals
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world.
Generalizing experimental findings: identification beyond adjustments
We aim to generalize the results of a randomized controlled trial (RCT) to a target population with the help of some observational data.
Clustering and Structural Robustness in Causal Diagrams
Graphs are commonly used to represent and visualize causal relations.
Identifying Causal Effects via Context-specific Independence Relations
We show that deciding causal effect non-identifiability is NP-hard in the presence of CSIs.
Estimation of causal effects with small data in the presence of trapdoor variables
This bias is related to variables that we call trapdoor variables.
Methodology Computation
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system.
Enhancing Identification of Causal Effects by Pruning
Causal models communicate our assumptions about causes and effects in real-world phe- nomena.
Simplifying Probabilistic Expressions in Causal Inference
Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference.
Surrogate Outcomes and Transportability
Identification of causal effects is one of the most fundamental tasks of causal inference.
Estimating complex causal effects from incomplete observational data
Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians.
Study design in causal models
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research.
