scholarly journals Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula

2011 ◽  
Vol 11 (4) ◽  
pp. 479-517 ◽  
Author(s):  
Rhian M. Daniel ◽  
Bianca L. De Stavola ◽  
Simon N. Cousens
Keyword(s):  
Indirect Effects ◽  
Related Problem ◽  
Causal Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Direct And Indirect Effects ◽  
Computation Procedure ◽  
Computation Formula ◽  
Intermediate Variables

This article describes a new command, gformula, that is an implementation of the g-computation procedure. It is used to estimate the causal effect of time-varying exposures on an outcome in the presence of time-varying confounders that are themselves also affected by the exposures. The procedure also addresses the related problem of estimating direct and indirect effects when the causal effect of the exposures on an outcome is mediated by intermediate variables, and in particular when confounders of the mediator–outcome relationships are themselves affected by the exposures. A brief overview of the theory and a description of the command and its options are given, and illustrations using two simulated examples are provided.

scholarly journals A generalized theory of separable effects in competing event settings

2021 ◽  
Author(s):  
Mats J. Stensrud ◽  
Miguel A. Hernán ◽  
Eric J Tchetgen Tchetgen ◽  
James M. Robins ◽  
Vanessa Didelez ◽  
...  
Keyword(s):  
Indirect Effects ◽  
Causal Effect ◽  
Time Varying ◽  
Direct And Indirect Effects ◽  
Loss To Follow Up ◽  
Causal Pathways ◽  
Common Causes ◽  
Competing Events ◽  
Definition Of ◽  
Weaker Conditions

AbstractIn competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects to define direct and indirect effects of the treatment on the event of interest. This definition was given in a simple setting, where the treatment was decomposed into two components acting along two separate causal pathways. Here we generalize the notion of separable effects, allowing for interpretation, identification and estimation in a wide variety of settings. We propose and discuss a definition of separable effects that is applicable to general time-varying structures, where the separable effects can still be meaningfully interpreted as effects of modified treatments, even when they cannot be regarded as direct and indirect effects. For these settings we derive weaker conditions for identification of separable effects in studies where decomposed, or otherwise modified, treatments are not yet available; in particular, these conditions allow for time-varying common causes of the event of interest, the competing events and loss to follow-up. We also propose semi-parametric weighted estimators that are straightforward to implement. We stress that unlike previous definitions of direct and indirect effects, the separable effects can be subject to empirical scrutiny in future studies.

scholarly journals Direct and Indirect Effects—An Information Theoretic Perspective

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 854 ◽  
Author(s):  
Gabriel Schamberg ◽  
William Chapman ◽  
Shang-Ping Xie ◽  
Todd P. Coleman
Keyword(s):  
Pacific Northwest ◽  
Southern Oscillation ◽  
Causal Effect ◽  
Climate Science ◽  
Causal Effects ◽  
Direct And Indirect Effects ◽  
General Applicability ◽  
Information Theoretic ◽  
The North ◽  
Linear Interactions

Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g., neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, they are fundamentally different from common statistical notions of causal influence in that they (1) compare distributions over the effect rather than values of the effect and (2) are defined with respect to random variables representing a cause rather than specific values of a cause. We here present IT measures of direct, indirect, and total causal effects. The proposed measures are unlike existing IT techniques in that they enable measuring causal effects that are defined with respect to specific values of a cause while still offering the flexibility and general applicability of IT techniques. We provide an identifiability result and demonstrate application of the proposed measures in estimating the causal effect of the El Niño–Southern Oscillation on temperature anomalies in the North American Pacific Northwest.

11. Identification and Estimation of Causal Effects with Time-Varying Treatments and Time-Varying Outcomes

2007 ◽  
Vol 37 (1) ◽  
pp. 393-434 ◽  
Author(s):  
Jennie E. Brand ◽  
Yu Xie
Keyword(s):  
Employment Status ◽  
Causal Effect ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Time Varying ◽  
Potential Outcome ◽  
Units Of Analysis ◽  
Wisconsin Longitudinal Study ◽  
Time Periods ◽  
Causal Variable

We develop an approach to identifying and estimating causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.

scholarly journals Causal inference under over-simplified longitudinal causal models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lola Étiévant ◽  
Vivian Viallon
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Repeated Measurements ◽  
Causal Models ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Time Varying ◽  
Weighted Averages

Abstract Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether – and how – causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.

scholarly journals Causal inference under multiple versions of treatment

2013 ◽  
Vol 1 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Tyler J. VanderWeele ◽  
Miguel A. Hernan
Keyword(s):  
Causal Inference ◽  
Treatment Effect ◽  
Indirect Effects ◽  
Causal Effect ◽  
Potential Outcomes ◽  
Direct And Indirect Effects ◽  
Treatment Variable ◽  
Effect Of Treatment ◽  
New Treatment ◽  
Potential Outcomes Framework

Abstract: In this article, we discuss causal inference when there are multiple versions of treatment. The potential outcomes framework, as articulated by Rubin, makes an assumption of no multiple versions of treatment, and here we discuss an extension of this potential outcomes framework to accommodate causal inference under violations of this assumption. A variety of examples are discussed in which the assumption may be violated. Identification results are provided for the overall treatment effect and the effect of treatment on the treated when multiple versions of treatment are present and also for the causal effect comparing a version of one treatment to some other version of the same or a different treatment. Further identification and interpretative results are given for cases in which the version precedes the treatment as when an underlying treatment variable is coarsened or dichotomized to create a new treatment variable for which there are effectively “multiple versions”. Results are also given for effects defined by setting the version of treatment to a prespecified distribution. Some of the identification results bear resemblance to identification results in the literature on direct and indirect effects. We describe some settings in which ignoring multiple versions of treatment, even when present, will not lead to incorrect inferences.

scholarly journals G-estimation of causal pathways in vocational rehabilitation for adults with psychotic disorders – a secondary analysis of a randomized trial

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ole Klungsøyr ◽  
June Ullevoldsæter Lystad ◽  
Helen Bull ◽  
Stig Evensen ◽  
Torill Ueland ◽  
...  
Keyword(s):  
Vocational Rehabilitation ◽  
Negative Symptoms ◽  
Causal Effect ◽  
Working Hours ◽  
Causal Effects ◽  
Psychotic Symptoms ◽  
Time Varying ◽  
Occupational Functioning ◽  
Data Set ◽  
Causal Pathways

Abstract Background Vocational rehabilitation (VR) has increasingly become an important intervention targeting poor occupational functioning in schizophrenia. The Norwegian Job Management Program (JUMP), sought to enhance occupational outcomes by augmenting VR with either cognitive behavioral therapy (CBT) techniques aiming to improve psychotic symptoms or cognitive remediation (CR) aiming to improve cognition. CBT is standard treatment in schizophrenia, but recent meta-analyses question the effect of CBT on negative psychotic symptoms. It is of interest to study the causal role of psychotic symptoms and cognitive functioning on occupational functioning. Methods Data from the JUMP VR – program, was reanalyzed with a causal inference method to assess the causal effects of reduced symptoms / improved neurocognitive functioning on occupational functioning measured by number of working hours per week. Participants (N = 131) had been randomized to either VR + CBT (N = 68) or VR + CR (N = 63). Large improvements in number of working hours were demonstrated in both intervention groups (nonsignificant group difference). G-estimation was used to assess the strength and nature of the causal effects, adjusted for time-varying confounding and selection – bias from loss to follow-up. Results Significant causal effects of reduction in each of four dimensions of symptoms and improved neurocognition respectively, on number of working hours were found (separate models). The effect of negative symptoms was the strongest and increased in magnitude during the whole observation period, while the effect of two other symptoms and neurocognition was constant. Adjusted for confounding (including potential feedback), the causal effect of a hypothetical change in negative symptoms equal to the average improvement in the CBT group corresponded to an increase in working hours of 3.2 h per week (95% CI: 1.11, 5.35). Conclusion High performance of g-estimation in a small psychiatric data set with few repeated measures and time-varying confounding and effects, was demonstrated. Augmented vocational rehabilitation showed causal effects of intervention targets with the strongest and increasing effect from negative symptoms on number of working hours. Combination of therapy and activation (indirect and direct approach) might explain improvement in both cognition and negative symptoms, and shed some light on effective ingredients for improved treatment of negative symptoms.

scholarly journals G-Estimation of Causal Pathways in Vocational Rehabilitation For Adults With Psychotic Disorders – A Secondary Analysis of A Randomized Trial

2021 ◽  
Author(s):  
Ole Klungsøyr ◽  
June Ullevoldsæter Lystad ◽  
Helen Bull ◽  
Stig Evensen ◽  
Torill Ueland ◽  
...  
Keyword(s):  
Vocational Rehabilitation ◽  
Negative Symptoms ◽  
Causal Effect ◽  
Working Hours ◽  
Causal Effects ◽  
Psychotic Symptoms ◽  
Time Varying ◽  
Occupational Functioning ◽  
Data Set ◽  
Causal Pathways

Abstract Background: Vocational rehabilitation (VR) has increasingly become an important intervention targeting poor occupational functioning in schizophrenia. The Norwegian Job Management Program (JUMP), sought to enhance occupational outcomes by augmenting VR with either cognitive behavioral therapy (CBT) techniques aiming to improve psychotic symptoms or cognitive remediation (CR) aiming to improve cognition. CBT is standard treatment in schizophrenia, but recent meta-analyses question the effect of CBT on negative psychotic symptoms. It is of interest to study the causal role of psychotic symptoms and cognitive functioning on occupational functioning. Methods: Data from the JUMP VR – program, was reanalyzed with a causal inference method to assess the causal effects of reduced symptoms / improved neurocognitive functioning on occupational functioning measured by number of working hours per week. Participants (N=131) had been randomized to either VR + CBT (N=68) or VR + CR (N=63). Large improvements in number of working hours were demonstrated in both intervention groups (nonsignificant group difference). G-estimation was used to assess the strength and nature of the causal effects, adjusted for time-varying confounding and selection – bias from loss to follow-up. Results: Significant causal effects of reduction in each of four dimensions of symptoms and improved neurocognition respectively, on number of working hours were found (separate models). The effect of negative symptoms was the strongest and increased in magnitude during the whole observation period, while the effect of the other symptoms and neurocognition was constant. Adjusted for confounding (including potential feedback), the causal effect of a hypothetical change in negative symptoms equal to the average improvement in the CBT group corresponded to an increase in working hours of 3.2 hours per week (95% CI: 1.11, 5.35).Conclusion: High performance of g-estimation in a small psychiatric data set with few repeated measures and time-varying confounding and effects, was demonstrated. Augmented vocational rehabilitation showed causal effects of intervention targets with the strongest and increasing effect from negative symptoms on number of working hours.Combination of therapy and activation (indirect and direct approach) might explain improvement in both cognition and negative symptoms, and shed some light on effective ingredients for improved treatment of negative symptoms.

The Decomposition of Correlation Coefficients into Causal Effects

1977 ◽  
Vol 9 (7) ◽  
pp. 805-812
Author(s):  
R N Davidson
Keyword(s):  
Correlation Coefficient ◽  
Indirect Effects ◽  
Correlation Matrix ◽  
Correlation Coefficients ◽  
Crime Rates ◽  
Causal Effects ◽  
Direct And Indirect Effects ◽  
Simple Correlation ◽  
Causal Inferences ◽  
Worked Example

The correlation coefficient is commonly used, yet its potential for drawing causal inferences is hardly tapped. Given even quite a simple correlation matrix, any coefficient may be readily decomposed into direct and indirect effects; joint effects with other specified variables; and coordinated error effects due to unspecified causes. A worked example relating to crime rates is given.

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