Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs

被引：0

作者：

Perkovic, Emilija ^{[1
]}

Textor, Johannes ^{[2
,3
]}

Kalisch, Markus ^{[1
]}

Maathuis, Marloes H. ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Seminar Stat, Zurich, Switzerland

[2] Radboud Univ Nijmegen, Med Ctr, Inst Comp & Informat Sci, Nijmegen, Netherlands

[3] Radboud Univ Nijmegen, Med Ctr, Dept Tumor Immunol, Nijmegen, Netherlands

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2018年 / 18卷

基金：

瑞士国家科学基金会;

关键词：

causal effects; graphical models; covariate adjustment; latent variables; confounding; DIRECTED ACYCLIC GRAPHS; CAUSAL INFERENCE; SELECTION; CONFOUNDER; LATENT;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 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.

引用

页数：62

共 56 条

[21] Covariate selection for estimating the causal effect of control plans by using causal diagrams
Kuroki, M
Miyakawa, M
[J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2003, 65 : 209 - 222
[22] Kuroki M., 2004, UNCERTAINTY ARTIF IN
[23] LAURITZEN SL, 1988, J ROY STAT SOC B MET, V50, P157
[24] INDEPENDENCE PROPERTIES OF DIRECTED MARKOV-FIELDS
LAURITZEN, SL
DAWID, AP
LARSEN, BN
LEIMER, HG
[J]. NETWORKS, 1990, 20 (05) : 491 - 505
[25] A GENERALIZED BACK-DOOR CRITERION
Maathuis, Marloes H.
Colombo, Diego
[J]. ANNALS OF STATISTICS, 2015, 43 (03) : 1060 - 1088
[26] Predicting causal effects in large-scale systems from observational data
Maathuis, Marloes H.
Colombo, Diego
Kalisch, Markus
Buehlmann, Peter
[J]. NATURE METHODS, 2010, 7 (04) : 247 - 248
[27] ESTIMATING HIGH-DIMENSIONAL INTERVENTION EFFECTS FROM OBSERVATIONAL DATA
Maathuis, Marloes H.
Kalisch, Markus
Buehlmann, Peter
[J]. ANNALS OF STATISTICS, 2009, 37 (6A) : 3133 - 3164
[28] Estimating bounds on causal effects in high-dimensional and possibly confounded systems
Malinsky, Daniel
Spirtes, Peter
[J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2017, 88 : 371 - 384
[29] Mardia K. V., 1980, MULTIVARIATE ANAL PR
[30] Meek C., 1995, Uncertainty in Artificial Intelligence. Proceedings of the Eleventh Conference (1995), P403

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