4 research outputs found
Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variable
among a large set of explanatory variables is of high practical importance in
many disciplines. Recent work in the field of causal discovery exploits
invariance properties of models across different experimental conditions for
detecting direct causal links. However, these approaches generally do not scale
well with the number of explanatory variables, are difficult to extend to
nonlinear relationships, and require data across different experiments.
Inspired by {\em Debiased} machine learning methods, we study a
one-vs.-the-rest feature selection approach to discover the direct causal
parent of the response. We propose an algorithm that works for purely
observational data, while also offering theoretical guarantees, including the
case of partially nonlinear relationships. Requiring only one estimation for
each variable, we can apply our approach even to large graphs, demonstrating
significant improvements compared to established approaches
Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework
Principled reasoning about the identifiability of causal effects from
non-experimental data is an important application of graphical causal models.
This paper focuses on effects that are identifiable by covariate adjustment, a
commonly used estimation approach. We present an algorithmic framework for
efficiently testing, constructing, and enumerating -separators in ancestral
graphs (AGs), a class of graphical causal models that can represent uncertainty
about the presence of latent confounders. Furthermore, we prove a reduction
from causal effect identification by covariate adjustment to -separation in
a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs
(MAGs). Jointly, these results yield constructive criteria that characterize
all adjustment sets as well as all minimal and minimum adjustment sets for
identification of a desired causal effect with multivariate exposures and
outcomes in the presence of latent confounding. Our results extend several
existing solutions for special cases of these problems. Our efficient
algorithms allowed us to empirically quantify the identifiability gap between
covariate adjustment and the do-calculus in random DAGs and MAGs, covering a
wide range of scenarios. Implementations of our algorithms are provided in the
R package dagitty.Comment: 52 pages, 20 figures, 12 table
On Searching for Generalized Instrumental Variables
Abstract Instrumental Variables are a popular way to identify the direct causal effect of a random variable X on a variable Y . Often no single instrumental variable exists, although it is still possible to find a set of generalized instrumental variables (GIVs) and identify the causal effect of all these variables at once. Till now it was not known how to find GIVs systematically or even test efficiently, if given variables satisfy GIV conditions. We provide fast algorithms for searching and testing restricted cases of GIVs. However, we prove that in the most general case it is NP-hard to verify if given variables fulfill the conditions of a general instrumental set