A Complete Generalized Adjustment Criterion

Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes.Comment: 10 pages, 6 figures, To appear in Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI2015

ICMHI 2021 (2021)

Causal artificial intelligence aims at developing bias-robust models that can be used to intervene on, rather than just be predictive, of risks or outcomes. However, learning interventional models from observational data, including electronic health records (EHR), is challenging due to inherent bias, e.g., protopathic, confounding, collider. When estimating the effects of treatment interventions, classical approaches like propensity score matching are often used, but they pose limitations with large feature sets, nonlinear/nonparallel treatment group assignments, and collider bias. In this work, we used data from a large EHR consortium -OneFlorida- and evaluated causal statistical/machine learning methods for determining the effect of statin treatment on the risk of Alzheimer's disease, a debated clinical research question. We introduced a combination of directed acyclic graph (DAG) learning and comparison with expert's design, with calculation of the generalized adjustment criterion (GAC), to find an optimal set of covariates for estimation of treatment effects -ameliorating collider bias. The DAG/CAC approach was assessed together with traditional propensity score matching, inverse probability weighting, virtual-twin/counterfactual random forests, and deep counterfactual networks. We showed large heterogeneity in effect estimates upon different model configurations. Our results did not exclude a protective effect of statins, where the DAG/GAC point estimate aligned with the maximum credibility estimate, although the 95% credibility interval included a null effect, warranting further studies and replication.U18 DP006512/DP/NCCDPHP CDC HHSUnited States/R21 CA245858/CA/NCI NIH HHSUnited States/UL1 TR001427/TR/NCATS NIH HHSUnited States/R01 CA246418/CA/NCI NIH HHSUnited States/U18DP006512/ACL/ACL HHSUnited States/R21 AG068717/AG/NIA NIH HHSUnited States

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 epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package ‘dagitty’, which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package ‘dagitty’ can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate ‘statistically equivalent’ but causally different DAGs; and identify exposure outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package ‘dagitty’ is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application ‘DAGitty’ is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http:// dagitty.net/]

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), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion unifies covariate adjustment for a large set of graph classes. Moreover, we define an explicit set that satisfies our criterion, if there is any set that satisfies our criterion. We also give efficient algorithms for constructing all sets that fulfill our criterion, implemented in the R package dagitty. Finally, we discuss the relationship between our criterion and other criteria for adjustment, and we provide new soundness and completeness proofs for the adjustment criterion for DAGs.Comment: 58 pages, 12 figures, to appear in JML

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment criteria and formulas from the acyclic setting to the general one (i.e. ioSCMs). Such criteria then allow to estimate (conditional) causal effects from observational data that was (partially) gathered under selection bias and cycles. This generalizes the backdoor criterion, the selection-backdoor criterion and extensions of these to arbitrary ioSCMs. Together, our results thus enable causal reasoning in the presence of cycles, latent confounders and selection bias. Finally, we extend the ID algorithm for the identification of causal effects to ioSCMs.Comment: Accepted for publication in Conference on Uncertainty in Artificial Intelligence 2019 (UAI-2019