Data-driven causal effect estimation based on graphical causal modelling: A survey
In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …
causal effects from non-experimental data is crucial for understanding the mechanism …
Causal inference and the data-fusion problem
E Bareinboim, J Pearl - Proceedings of the National …, 2016 - National Acad Sciences
We review concepts, principles, and tools that unify current approaches to causal analysis
and attend to new challenges presented by big data. In particular, we address the problem …
and attend to new challenges presented by big data. In particular, we address the problem …
Robust causal inference using directed acyclic graphs: the R package 'dagitty'
Directed acyclic graphs (DAGs), which offer systematic representations of causal
relationships, have become an established framework for the analysis of causal inference in …
relationships, have become an established framework for the analysis of causal inference in …
Causal imitation learning with unobserved confounders
One of the common ways children learn is by mimicking adults. Imitation learning focuses on
learning policies with suitable performance from demonstrations generated by an expert …
learning policies with suitable performance from demonstrations generated by an expert …
Equality of opportunity in classification: A causal approach
J Zhang, E Bareinboim - Advances in neural information …, 2018 - proceedings.neurips.cc
Abstract The Equalized Odds (for short, EO) is one of the most popular measures of
discrimination used in the supervised learning setting. It ascertains fairness through the …
discrimination used in the supervised learning setting. It ascertains fairness through the …
A Review of Some Recent Advances in Causal Inference.
MH Maathuis, P Nandy - Handbook of big data, 2016 - api.taylorfrancis.com
Causal questions are fundamental in all parts of science. Answering such questions from
observational data is notoriously difficult, but there has been a lot of recent interest and …
observational data is notoriously difficult, but there has been a lot of recent interest and …
Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs
We present a graphical criterion for covariate adjustment that is sound and complete for four
different classes of causal graphical models: directed acyclic graphs (DAGs), maximal …
different classes of causal graphical models: directed acyclic graphs (DAGs), maximal …
A generalized back-door criterion
MH Maathuis, D Colombo - 2015 - projecteuclid.org
We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more
general types of graphs that describe Markov equivalence classes of DAGs and/or allow for …
general types of graphs that describe Markov equivalence classes of DAGs and/or allow for …
Disentangle and remerge: interventional knowledge distillation for few-shot object detection from a conditional causal perspective
Few-shot learning models learn representations with limited human annotations, and such a
learning paradigm demonstrates practicability in various tasks, eg, image classification …
learning paradigm demonstrates practicability in various tasks, eg, image classification …
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
P Nandy, MH Maathuis, TS Richardson - 2017 - projecteuclid.org
Estimating the effect of joint interventions from observational data in sparse high-dimensional
settings Page 1 The Annals of Statistics 2017, Vol. 45, No. 2, 647–674 DOI: 10.1214/16-AOS1462 …
settings Page 1 The Annals of Statistics 2017, Vol. 45, No. 2, 647–674 DOI: 10.1214/16-AOS1462 …