Open Access Published by De Gruyter

Volume 2 Issue 1 -

Issue of Journal of Causal Inference

Submit manuscript

Contents
Journal Overview

Publicly Available April 18, 2014

Frontmatter

Page range: i-iv

Download PDF

Publicly Available April 18, 2014

Monotone Confounding, Monotone Treatment Selection and Monotone Treatment Response

Zhichao Jiang, Yasutaka Chiba, Tyler J. VanderWeele

Page range: 1-12

More Download PDF

Abstract

Manski (Monotone treatment response. Econometrica 1997;65:1311–34) and Manski and Pepper (Monotone instrumental variables: with an application to the returns to schooling. Econometrica 2000;68:997–1010) gave sharp bounds on causal effects under the assumptions of monotone treatment response (MTR) and monotone treatment selection (MTS). VanderWeele (The sign of the bias of unmeasured confounding. Biometrics 2008;64:702–6) provided bounds for binary treatment under an assumption of monotone confounding (MC). We discuss the relation between MC and MTS and provide bounds under various combinations of these assumptions. We show that MC and MTS coincide for a binary treatment, but MC does not imply MTS for a treatment variable with more than two levels.

Publicly Available January 14, 2014

Causal Inference for a Population of Causally Connected Units

Mark J. van der Laan

Page range: 13-74

More Download PDF

Abstract

Suppose that we observe a population of causally connected units. On each unit at each time-point on a grid we observe a set of other units the unit is potentially connected with, and a unit-specific longitudinal data structure consisting of baseline and time-dependent covariates, a time-dependent treatment, and a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention. Causal effects of interest are defined as contrasts of the mean of the unit-specific outcomes under different stochastic interventions one wishes to evaluate. This covers a large range of estimation problems from independent units, independent clusters of units, and a single cluster of units in which each unit has a limited number of connections to other units. The allowed dependence includes treatment allocation in response to data on multiple units and so called causal interference as special cases. We present a few motivating classes of examples, propose a structural causal model, define the desired causal quantities, address the identification of these quantities from the observed data, and define maximum likelihood based estimators based on cross-validation. In particular, we present maximum likelihood based super-learning for this network data. Nonetheless, such smoothed/regularized maximum likelihood estimators are not targeted and will thereby be overly bias w.r.t. the target parameter, and, as a consequence, generally not result in asymptotically normally distributed estimators of the statistical target parameter. To formally develop estimation theory, we focus on the simpler case in which the longitudinal data structure is a point-treatment data structure. We formulate a novel targeted maximum likelihood estimator of this estimand and show that the double robustness of the efficient influence curve implies that the bias of the targeted minimum loss-based estimation (TMLE) will be a second-order term involving squared differences of two nuisance parameters. In particular, the TMLE will be consistent if either one of these nuisance parameters is consistently estimated. Due to the causal dependencies between units, the data set may correspond with the realization of a single experiment, so that establishing a (e.g. normal) limit distribution for the targeted maximum likelihood estimators, and corresponding statistical inference, is a challenging topic. We prove two formal theorems establishing the asymptotic normality using advances in weak-convergence theory. We conclude with a discussion and refer to an accompanying technical report for extensions to general longitudinal data structures.

Publicly Available April 18, 2014

Confounding Equivalence in Causal Inference

Judea Pearl, Azaria Paz

Page range: 75-93

More Download PDF

Abstract

The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the back-door criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We further extend the test to include treatment-dependent covariates by broadening the back-door criterion and establishing equivalence of adjustment under selection bias conditions. Applications to covariate selection and model testing are discussed.

Publicly Available March 22, 2014

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

Anna L. Decker, Alan Hubbard, Catherine M. Crespi, Edmund Y.W. Seto, May C. Wang

Page range: 95-108

More Download PDF

Abstract

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multivariable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice.

Causal, Casual and Curious

Publicly Available April 18, 2014

Is Scientific Knowledge Useful for Policy Analysis? A Peculiar Theorem Says: No

Judea Pearl

Page range: 109-112

More Download PDF

Abstract

Conventional wisdom dictates that the more we know about a problem domain the easier it is to predict the effects of policies in that domain. Strangely, this wisdom is not sanctioned by formal analysis, when the notions of “knowledge” and “policy” are given concrete definitions in the context of nonparametric causal analysis. This note describes this peculiarity and speculates on its implications.

Erratum

Publicly Available February 11, 2014

Erratum to Revisiting a Discrepant Result: A Propensity Score Analysis, the Paired Availability Design for Historical Controls, and a Meta-Analysis of Randomized Trials [J Causal Inference DOI: 10.1515/jci-2013-0005]

Stuart G. Baker, Karen S. Lindeman

Page range: 113-113

Download PDF

About this journal

===============

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!

===============

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).