Invariant Causal Prediction for Sequential Data

We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X 1 , ..., X d ). Classical ordinary least-square regression includes all predictors that reduce the variance of Y. Using only the causal predictors instead leads to models that have...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American Statistical Association Jg. 114; H. 527; S. 1264 - 1276
Hauptverfasser: Pfister, Niklas, Bühlmann, Peter, Peters, Jonas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria Taylor & Francis 03.07.2019
Taylor & Francis Group, LLC
Taylor & Francis Ltd
Schlagworte:
Asymptotic methods
Causal structure learning
Causality
Change point model
Chow statistic
Confidence intervals
Data
Economic forecasting
Economic policy
equations
Heterogeneity
Instantaneous causal effects
Intervention
Invariants
Macroeconomics
Monetary policy
prediction
Regression analysis
Statistical analysis
Statistical methods
Statistics
Theory and Methods
Time series
time series analysis
variance
ISSN:0162-1459, 1537-274X, 1537-274X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X 1 , ..., X d ). Classical ordinary least-square regression includes all predictors that reduce the variance of Y. Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions; loosely speaking they lead to invariance across different "environments" or "heterogeneity patterns." More precisely, the conditional distribution of Y given its causal predictors is the same for all observations, provided that there are no interventions on Y. Recent work exploits such a stability to infer causal relations from data with different but known environments. We show that even without having knowledge of the environments or heterogeneity pattern, inferring causal relations is possible for time-ordered (or any other type of sequentially ordered) data. In particular, this allows detecting instantaneous causal relations in multivariate linear time series, which is usually not the case for Granger causality. Besides novel methodology, we provide statistical confidence bounds and asymptotic detection results for inferring causal predictors, and present an application to monetary policy in macroeconomics. Supplementary materials for this article are available online.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2018.1491403