G-computation estimation for causal inference with complex longitudinal data

2006 ◽  
Vol 51 (3) ◽  
pp. 1676-1697 ◽  
Author(s):  
Romain Neugebauer ◽  
Mark J. van der Laan
Keyword(s):  
Longitudinal Data ◽  
Causal Inference

scholarly journals Joint mixed-effects models for causal inference with longitudinal data

2017 ◽  
Vol 37 (5) ◽  
pp. 829-846 ◽  
Author(s):  
Michelle Shardell ◽  
Luigi Ferrucci
Keyword(s):  
Longitudinal Data ◽  
Causal Inference ◽  
Mixed Effects ◽  
Mixed Effects Models

scholarly journals Statistical methodologies for drawing causal inference from an unreplicated farmlet experiment conducted by the Cicerone Project

2013 ◽  
Vol 53 (8) ◽  
pp. 643 ◽  
Author(s):  
R. Murison ◽  
J. M. Scott
Keyword(s):  
Longitudinal Data ◽  
Statistical Model ◽  
Field Experiment ◽  
Causal Inference ◽  
Statistical Inference ◽  
Treatment Effects ◽  
Management Strategies ◽  
Grazing Management ◽  
Botanical Composition ◽  
Longitudinal Profiles

The present paper explains the statistical inference that can be drawn from an unreplicated field experiment that investigated three different pasture and grazing management strategies. The experiment was intended to assess these three strategies as whole farmlet systems where scale of the experiment precluded replication. The experiment was planned so that farmlets were allocated to matched paddocks on the basis of background variables that were measured across each paddock before the start of the experiment. These conditioning variables were used in the statistical model so that farmlet effects could be discerned from the longitudinal profiles of the responses. The purpose is to explain the principles by which longitudinal data collected from the experiment were interpreted. Two datasets, including (1) botanical composition and (2) hogget liveweights, are used in the present paper as examples. Inferences from the experiment are guarded because we acknowledge that the use of conditioning variables and matched paddocks does not provide the same power as replication. We, nevertheless, conclude that the differences observed are more likely to have been due to treatment effects than to random variation or bias.

Causal inference and longitudinal data: a case study of religion and mental health

2016 ◽  
Vol 51 (11) ◽  
pp. 1457-1466 ◽  
Author(s):  
Tyler J. VanderWeele ◽  
John W. Jackson ◽  
Shanshan Li
Keyword(s):  
Mental Health ◽  
Longitudinal Data ◽  
Causal Inference

scholarly journals These are not the effects you are looking for: Causality and the within-/between-person distinction in longitudinal data analysis

2021 ◽  
Author(s):  
Julia M. Rohrer ◽  
Kou Murayama
Keyword(s):  
Data Analysis ◽  
Longitudinal Data ◽  
Causal Inference ◽  
Longitudinal Data Analysis ◽  
Decisive Role ◽  
Longitudinal Models ◽  
Technical Literature ◽  
Psychological Science ◽  
Conceptual Clarity ◽  
The Relationship

In psychological science, researchers often pay particular attention to the distinction between within- and between-person relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-person distinction and causal inference, and show that the distinction is informative but does not play a decisive role for causal inference. Our main points are threefold. First, within-person data are not necessary for causal inference; for example, between-person experiments can inform us about (average) causal effects. Second, within-person data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-person associations. Finally, despite not being sufficient, within-person data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models, and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions we ask, we should start with well-defined theoretical estimands which in turn determine both study design and data analysis.

Estimation of pre- and posttreatment average treatment effects with binary time-varying treatment using Stata

2019 ◽  
Vol 19 (3) ◽  
pp. 551-565 ◽  
Author(s):  
Giovanni Cerulli ◽  
Marco Ventura
Keyword(s):  
Program Evaluation ◽  
Longitudinal Data ◽  
Causal Inference ◽  
Graphical Representation ◽  
Causal Effects ◽  
Necessary Condition ◽  
Time Varying ◽  
Difference In Differences ◽  
Average Treatment Effects ◽  
The Difference

In this article, we describe tvdiff, a community-contributed command that implements a generalization of the difference-in-differences estimator to the case of binary time-varying treatment with pre- and postintervention periods. tvdiff is flexible and can accommodate many actual situations, enabling the user to specify the number of pre- and postintervention periods and a graphical representation of the estimated coefficients. In addition, tvdiff provides two distinct tests for the necessary condition of the identification of causal effects, namely, two tests for the so-called parallel-trend assumption. tvdiff is intended to simplify applied works on program evaluation and causal inference when longitudinal data are available.

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