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Volume 6 Issue 1

Issue of Journal of Causal Inference

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Contents
Journal Overview

Research Articles

Open Access October 28, 2017

Detecting Confounding in Multivariate Linear Models via Spectral Analysis

Dominik Janzing, Bernhard Schölkopf

Article number: 20170013

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Abstract

We study a model where one target variable Y$Y$ is correlated with a vector X:=(X1,…,Xd)$\textbf{X}:=(X_1,\dots,X_d)$ of predictor variables being potential causes of Y$Y$. We describe a method that infers to what extent the statistical dependences between X$\textbf{X}$ and Y$Y$ are due to the influence of X$\textbf{X}$ on Y$Y$ and to what extent due to a hidden common cause (confounder) of X$\textbf{X}$ and Y$Y$. The method relies on concentration of measure results for large dimensions d$d$ and an independence assumption stating that, in the absence of confounding, the vector of regression coefficients describing the influence of each X$\textbf{X}$ on Y$Y$ typically has ‘generic orientation’ relative to the eigenspaces of the covariance matrix of X$\textbf{X}$. For the special case of a scalar confounder we show that confounding typically spoils this generic orientation in a characteristic way that can be used to quantitatively estimate the amount of confounding (subject to our idealized model assumptions).

Open Access December 8, 2017

Determinantal Generalizations of Instrumental Variables

Luca Weihs, Bill Robinson, Emilie Dufresne, Jennifer Kenkel, Kaie Kubjas Reginald McGee II, McGee II Reginald, Nhan Nguyen, Elina Robeva, Mathias Drton

Article number: 20170009

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Abstract

Linear structural equation models relate the components of a random vector using linear interdependencies and Gaussian noise. Each such model can be naturally associated with a mixed graph whose vertices correspond to the components of the random vector. The graph contains directed edges that represent the linear relationships between components, and bidirected edges that encode unobserved confounding. We study the problem of generic identifiability, that is, whether a generic choice of linear and confounding effects can be uniquely recovered from the joint covariance matrix of the observed random vector. An existing combinatorial criterion for establishing generic identifiability is the half-trek criterion (HTC), which uses the existence of trek systems in the mixed graph to iteratively discover generically invertible linear equation systems in polynomial time. By focusing on edges one at a time, we establish new sufficient and new necessary conditions for generic identifiability of edge effects extending those of the HTC. In particular, we show how edge coefficients can be recovered as quotients of subdeterminants of the covariance matrix, which constitutes a determinantal generalization of formulas obtained when using instrumental variables for identification. While our results do not completely close the gap between existing sufficient and necessary conditions we find, empirically, that our results allow us to prove the generic identifiability of many more mixed graphs than the prior state-of-the-art.

Open Access December 9, 2017

Variable Selection in Causal Inference using a Simultaneous Penalization Method

Ashkan Ertefaie, Masoud Asgharian, David A. Stephens

Article number: 20170010

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Abstract

In the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in estimation of the treatment effect. We propose a variable selection method using a penalized objective function that is based on both the outcome and treatment assignment models. The proposed method facilitates confounder selection in high-dimensional settings. We show that under some mild conditions our method attains the oracle property. The selected variables are used to form a doubly robust regression estimator of the treatment effect. Using the proposed method we analyze a set of data on economic growth and study the effect of life expectancy as a measure of population health on the average growth rate of gross domestic product per capita.

Causal, Casual and Curious

Publicly Available March 2, 2018

What is Gained from Past Learning

Judea Pearl

Article number: 20180005

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Abstract

We consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.

About this journal

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Special Issues

Neyman (1923) and its influences on causal inference
COORDINATING EDITOR: Peng Ding (University of California, USA)
Deadline for submissions: October 1, 2023

Journal of Causal Inference will publish a Special Issue in cooperation with the 2023 Interactive Causal Learning Conference.

More information soon!

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Objective
Journal of Causal Inference (JCI) is a fully peer-reviewed, open access, electronic-only journal. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-term preservation, no space constraints.

JCI publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.

The past two decades have seen causal inference emerge as a unified field with a solid theoretical foundation, useful in many of the empirical and behavioral sciences. Journal of Causal Inference aims to provide a common venue for researchers working on causal inference in biostatistics and epidemiology, economics, political science and public policy, cognitive science and formal logic, and any field that aims to understand causality. The journal serves as a forum for this growing community to develop a shared language and study the commonalities and distinct strengths of their various disciplines' methods for causal analysis.

Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression or simultaneous equations, glossing over the special precautions demanded by causal analysis. In contrast, JCI highlights both the uniqueness and interdisciplinary nature of causal research.

Topics
Any field aiming at understanding causality, especially

Biostatistics and epidemiology
Computer Science
Economics
Machine Learning
Political science
Public policy
Cognitive science
Formal logic

Causal inference:

Research design
Causal model and target parameter specification
Identifiability
Statistical estimation
Sensitivity analysis/interpretation.
Quantitative statistics’ elaboration of causal methods in applied data analyses
Cross-disciplinary methodological research
History of the causal inference field and its philosophical underpinnings

Article formats
Original research articles, book reviews, short communications on topics that aim to stimulate public debate and bring unorthodox perspectives to open questions

Open Access model
Due to the switch to Open Access model beginning from 2020, the Journal of Causal Inference is subject to an Article Processing Charge. The standard APC amounts to €1000. However, covering this fee is not mandatory and depends on the authors’ financial ability. The Journal offers extensive DISCOUNT and WAIVER policy, so we strongly encourage the authors to contact us and discuss their willingness to cover the APC (or part of it). There are no submission charges – Article Processing Charges apply after the acceptance of a manuscript. For more details, please refer to the Article Processing Charges document (in PDF).