On efficient adjustment in causal graphs Article Swipe

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Janine Witte , Leonard Henckel , Marloes H. Maathuis , Vanessa Didelez ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.3929/ethz-b-000459196

We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustment sets are, however, not unique. Recent research has introduced a graphical criterion for an 'optimal' valid adjustment set (O-set). For a given graph, adjustment by the O-set yields the smallest asymptotic variance compared to other adjustment sets in certain parametric and non-parametric models. In this paper, we provide three new results on the O-set. First, we give a novel, more intuitive graphical characterisation: We show that the O-set is the parent set of the outcome node(s) in a suitable latent projection graph, which we call the forbidden projection. An important property is that the forbidden projection preserves all information relevant to total causal effect estimation via covariate adjustment, making it a useful methodological tool in its own right. Second, we extend the existing IDA algorithm to use the O-set, and argue that the algorithm remains semi-local. This is implemented in the R-package pcalg. Third, we present assumptions under which the O-set can be viewed as the target set of popular non-graphical variable selection algorithms such as stepwise backward selection.

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Theoretical Computer Science

Artificial Intelligence

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Covariate Set (abstract data type) Causal structure Parametric statistics Graph Projection (relational algebra) Mathematics Graphical model Algorithm Computer science Theoretical computer science Artificial intelligence Statistics Programming language Physics Quantum mechanics

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Type: article
Language: en
Landing Page: http://hdl.handle.net/20.500.11850/459196
PDF: http://hdl.handle.net/20.500.11850/459196
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Cited By: 8
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DOI: https://doi.org/10.3929/ethz-b-000459196

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Title: On efficient adjustment in causal graphs

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Language: en

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Publication year: 2020

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Publication date: 2020-01-01

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Authors: Janine Witte, Leonard Henckel, Marloes H. Maathuis, Vanessa Didelez

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Landing page: https://hdl.handle.net/20.500.11850/459196

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Concepts: Covariate, Set (abstract data type), Causal structure, Parametric statistics, Graph, Projection (relational algebra), Mathematics, Graphical model, Algorithm, Computer science, Theoretical computer science, Artificial intelligence, Statistics, Programming language, Physics, Quantum mechanics

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Cited by: 8

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abstract_inverted_index.viewed	187
abstract_inverted_index.yields	61
abstract_inverted_index.Second,	152
abstract_inverted_index.certain	72
abstract_inverted_index.models.	76
abstract_inverted_index.node(s)	109
abstract_inverted_index.outcome	108
abstract_inverted_index.popular	193
abstract_inverted_index.present	179
abstract_inverted_index.provide	81
abstract_inverted_index.remains	168
abstract_inverted_index.results	84
abstract_inverted_index.unique.	38
abstract_inverted_index.(O-set).	52
abstract_inverted_index.Ideally,	14
abstract_inverted_index.backward	201
abstract_inverted_index.compared	66
abstract_inverted_index.consider	1
abstract_inverted_index.existing	156
abstract_inverted_index.however,	36
abstract_inverted_index.property	124
abstract_inverted_index.relevant	133
abstract_inverted_index.research	40
abstract_inverted_index.selected	18
abstract_inverted_index.smallest	63
abstract_inverted_index.stepwise	200
abstract_inverted_index.suitable	112
abstract_inverted_index.variable	195
abstract_inverted_index.variance	65
abstract_inverted_index.'optimal'	48
abstract_inverted_index.R-package	175
abstract_inverted_index.algorithm	158, 167
abstract_inverted_index.covariate	12, 140
abstract_inverted_index.criterion	45
abstract_inverted_index.forbidden	120, 128
abstract_inverted_index.graphical	44, 95
abstract_inverted_index.important	123
abstract_inverted_index.intuitive	94
abstract_inverted_index.knowledge	26
abstract_inverted_index.preserves	130
abstract_inverted_index.selection	196
abstract_inverted_index.adjustment	15, 33, 50, 57, 69
abstract_inverted_index.algorithms	197
abstract_inverted_index.asymptotic	64
abstract_inverted_index.estimation	2, 138
abstract_inverted_index.introduced	42
abstract_inverted_index.parametric	73
abstract_inverted_index.projection	114, 129
abstract_inverted_index.reflecting	25
abstract_inverted_index.selection.	202
abstract_inverted_index.structure.	31
abstract_inverted_index.underlying	29
abstract_inverted_index.adjustment,	141
abstract_inverted_index.adjustment.	13
abstract_inverted_index.assumptions	180
abstract_inverted_index.implemented	172
abstract_inverted_index.information	132
abstract_inverted_index.projection.	121
abstract_inverted_index.semi-local.	169
abstract_inverted_index.non-graphical	194
abstract_inverted_index.observational	9
abstract_inverted_index.methodological	146
abstract_inverted_index.non-parametric	75
abstract_inverted_index.characterisation:	96
cited_by_percentile_year.max	97
cited_by_percentile_year.min	90
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile.value	0.85755014
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False