 Research article
 Open access
 Published:
Gcomputation of average treatment effects on the treated and the untreated
BMC Medical Research Methodology volume 17, Article number: 3 (2017)
Abstract
Background
Average treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub) population. In this paper we illustrate the steps for estimating ATT and ATU using gcomputation implemented via Monte Carlo simulation.
Methods
To obtain marginal effect estimates for ATT and ATU we used a threestep approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention.
Results
The estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. In our illustrative example, the effect (risk difference [RD]) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was −0.019 (95% CI: −0.040, −0.007) and that among those who have less than a high school education in India (ATU) was −0.012 (95% CI: −0.036, 0.010).
Conclusions
The gcomputation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. Its use should be encouraged in modern epidemiologic teaching and practice.
Background
In epidemiology, (bio)statistics and related fields, researchers are often interested in the average treatment effect in the total population (average treatment effect, ATE). This quantity provides the average difference in outcome between units assigned to the treatment and units assigned to the placebo (control) [1]. However, in economics and evaluation studies, it has been noted that the average treatment effect among units who actually receive the treatment or intervention (average treatment effects on the treated, ATT) may be the implicit quantity sought and the most relevant to policy makers [2]. For instance, consider a scenario where a government has implemented a smoking cessation campaign intervention to decrease the smoking prevalence in a city and now wishes to evaluate the impact of such intervention. Although the overarching goal of such evaluation may be to assess the impact of such intervention in reducing the prevalence of smoking in the general population (i.e. ATE), researchers and policymakers might be interested in explicitly evaluating the effect of the intervention on those who actually received the intervention (i.e. ATT) but not that on those among whom the intervention was never intended.
Alternatively, researchers may be interested in estimating the potential impact of an existing program in a new target (sub) population. For instance, one might wish to project the effect of the smoking cessation intervention in a city that did not receive the intervention in order to gauge its potential impact when such intervention is actually implemented. This latter quantity is referred to as the average treatment effect on the untreated (ATU). Interestingly, the ATE can be seen as a weighted average of the ATT and the ATU. All three quantities will be equal when the covariate distribution is the same among the treated and the untreated (e.g. under perfect randomization with perfect compliance or when there is no unmeasured confounders) and there is no effect measure modification by the covariates.
Robins introduced the “gmethods” to estimate such quantities using observational data [3]. Among these, the marginal structural models (MSMs) were designed to estimate marginal quantities (i.e., not conditional on other covariates). The parameters of a MSM can be consistently estimated using two classes of estimators: the gcomputation algorithm [4] and the inverseprobability of treatment weighting (IPTW) [5]. Gcomputation is often seen as a viable alternative to IPTW because gcomputation produces more efficient (i.e. small standard errors) and more stable estimates in parametric settings and can better handle heterogeneity involving timevarying exposure and confounding [6]. To date, there are several didactic demonstrations for gcomputation [7, 8] and applied examples for projecting the impact of hypothetical interventions aimed at reducing risk factors for coronary heart diseases [9] or diabetes in adult populations [10], or at reducing early childhood adiposity [11]. However, these studies focused on ATE and there are still no accessible demonstrations of gcomputation [4] applied to ATT and ATU. This manuscript aims to present an easytouse gcomputation technique using Monte Carlo simulation for consistently estimating ATT and ATU. We also present alternative ways to obtain ATT and ATU via ATE with sample restriction or gcomputation technique without simulation.
Methods
Notation and gcomputation steps
In the remaining, we will use capital letters to refer to random variables and lowercase letters to represent the specific realizations of the corresponding random variables. Let A denote the treatment, with a and a ^{*} as its index and reference values, Y the outcome, C a set of covariates sufficient for confounding control, and Y _{ a } the potential outcome that would have occurred had treatment A, perhaps contrary to fact, been set to a. Each subject in the population has a pair of potential outcomes, one being observed and the other being counterfactual. Y _{ a } is the observed outcome had the subject received the treatment A = a whereas Y _{ a* } is the counterfactual outcome. Conversely, for subjects who receive placebo (control), Y _{ a* } is the observed outcome while Y _{ a } is the counterfactual outcome. The ATE, defined as \( E\left({Y}_a{Y}_{a^{*}}\right) \), is the average marginal treatment effect in the total population. The ATT, defined as \( E\left({Y}_a{Y}_{a^{*}}\BigA=a\right) \) and the ATU, defined as \( E\left({Y}_a{Y}_{a^{*}}\BigA={a}^{*}\right) \), measure the marginal treatment effect in the subpopulation that received the treatment and the subpopulation that did not, respectively. When the assumptions of consistency [12], conditional exchangeability given C [13], and positivity [14] are met, the target causal parameters ATE, ATT and ATU on the risk difference scale can be estimated using observational data and the following estimators:
Steps to implement gcomputation using Monte Carlo simulation are as follows:
Step 1: Fit a flexible model for Y on A and covariates C (i.e. with all possible and relevant interaction terms) and save the regression coefficients.
Step 2: Resample the original data with replacement K times (e.g. 200 or as many as computationally feasible). Create two copies of this pooled dataset and stack them. Assign a new treatment intervention variable A = a for every observation in the first copy and A = a ^{*} in the second copy. Then, generate potential outcomes for ATT and ATU separately using the regression coefficients obtained from step 1. For ATT, assign the potential outcome Y _{ a } for treated (i.e. A = a) individuals as their observed outcome Y in the “intervention A = a” dataset copy (by consistency), but impute their counterfactual outcome Y _{ a* } in the “intervention A = a ^{*}” copy (by conditional exchangeability). This latter counterfactual outcome is simulated under nontreatment, based on the outcome model and regression coefficients from step 1. For ATU, by consistency assumption, assign the potential outcome Y _{ a* } = Y among the untreated (i.e. A = a ^{*}) individuals in the “intervention A = a ^{*}” dataset copy, and impute their counterfactual outcome Y _{ a } in the “intervention A = a” copy. This counterfactual outcome Y _{ a } is simulated under treatment, based on the outcome model and regression coefficients from step 1. Note that the gcomputation of the ATT or ATU involves imputing or simulating only half of the potential outcomes under the counterfactual treatment since by consistency under factual treatment the potential outcome is observed.
Step 3: For ATT and ATU respectively, regress the corresponding potential outcome variable on the intervention variable A for the entire pooled simulated sample to obtain the point estimate. Repeat steps 1 to 3 on J (e.g. 500) bootstrapped samples taken at random with replacement from the original data. We obtain the standard errors (SEs) and 95% confidence intervals (CIs) based on the J resultant point estimates from the final regression in step 3. The standard deviation of these J point estimates is taken as the standard error and the corresponding 2.5th and 97.5th percentiles are taken as the confidence limits of the 95% CI. Nonparametric bootstrapping [15] can also be used to obtain biascorrected and accelerated CIs.
One could also obtain ATT and ATU from average treatment effect (ATE) by simply restricting the analysis for ATE estimation to the treated (for ATT) or to the untreated (for ATU) (Additional file 1: Section 1). An alternative gcomputation technique without simulation is included in the Additional file 1: Section2.
Illustrations
We applied the above simulation method to the India sample data from the crosssectional World Health Survey (WHS) conducted by the WHO from 2002 to 2004 [16]. Samples were probabilistically selected with every individual being assigned to a known nonzero selection probability. All participants were interviewed facetoface with the standardized WHS survey, which included questions regarding demographic, socioeconomic and behavioral factors. Details of dataset description and variable creation can be found elsewhere [17].
Table 1 displays the estimates for ATT, ATU and ATE on the risk difference and odds ratio scale respectively for binary education (treatment) and binary angina indicator (outcome), accounting for age and gender (covariates). We were interested in estimating the impact of a hypothetical intervention (aimed at ensuring that the target study participants have at least a high school education) on angina diagnosis. The intervention could be implemented (i) universally in the whole population of India (ATE), (ii) among individuals of a subpopulation of India who actually completed high school or had higher educational attainment (ATT), or (iii) among individuals of a subpopulation of India who had less than a high school education (ATU) when the survey was conducted. Detailed steps and the accompanying SAS codes for this illustrative example are included in the Additional file 1: Section 3 and Additional file 1: Section 5.
Results
In the illustration, participants with at least a high school education were less likely to report having an angina diagnosis compared to those with less than a high school education, based on both risk difference (RD) and odds ratio (OR) measures (Table 1). The estimates for ATT, ATU and ATE were of similar magnitude, with ATE being in between ATT and ATU as expected. The ATT estimates were of slightly greater magnitude (RD: −0.019, 95% CI: −0.040, −0.007; OR: 0.773, 95% CI: 0.607, 0.944) than the ATU estimates (RD: −0.012, 95% CI: −0.036, 0.010; OR: 0.910, 95% CI: 0.678, 1.177), suggesting that the protective effect of a higher education on angina may be stronger among the participants who indeed have at least a high school education than among those who have less than a high school education in India.
Similar results obtained via gcomputation without simulation are presented in the Additional file 1: Table S1.
Discussions
In this article, we presented a 3step approach to estimating ATT and ATU via Monte Carlo simulation. Since ATE risk difference is the weighted average of ATT and ATU, weighted by the relative sample size of those who are treated and untreated, ATT and ATU can also be estimated from ATE via sample restriction.
When generating the potential outcomes in step 2, the potential outcome will be the same as the observed outcome if the intervention assignment (e.g. treatment) is indeed what the subject originally received and the consistency assumption is satisfied. Accordingly, the counterfactual outcome for the same subjects will be imputed (simulated) based on the outcome from those who received the alternative to treatment (e.g. placebo) and are comparable (i.e., exchangeable) conditional on measured covariates or confounders, if the assumption of conditional exchangeability assumption is met. In step 2 of the alternative gcomputation approach that does not require simulation, the predicted outcomes [i.e., E(YA = a, C = c)] are generated for both treated and untreated individuals. While the approach via simulation clearly demonstrates the importance of the two core assumptions—consistency and conditional exchangeability—to estimate causal parameters from observational data, the approach without simulation is less computationally intensive.
We also need the positivity assumption which requires that there exist participants who experienced all levels of the treatment (such as being treated or untreated) for every combination of the values of the observed confounders in the population under study [14]. This latter assumption needs to be supported by the data at hand. Steps for implementing gcomputation for ATT and ATU allow us to better understand the importance of assumptions that are often listed but seldom discussed.
Besides the consistency, conditional exchangeability and positivity assumptions, other implicit assumptions such as the absence of other biases (selection bias and measurement error) and correct model specification need to be satisfied in order to estimate ATE, ATT and ATU consistently. Gcomputation relies heavily on outcome model specification as shown in the above steps, in which we used the regression coefficients we obtained from the outcome regression model in step 1 to predict potential outcomes. On the contrary, the IPTW method relies on correct exposure model specification assumptions. Therefore, these two gmethods can sometimes yield different results. Their strengths and limitations, and performance under violation of the positivity assumption have also been discussed in the literature [6, 18]. When possible, researchers could use both methods, or use doubly robust methods [19–21] where consistent estimates for the target effects can be obtained as long as either the outcome or exposure model is correctly specified.
Conclusion
The gcomputation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU, beyond routine age and sexstandardization and as an alternative to IPTW fitting of MSM [22]. It should be used in modern epidemiologic teaching and practice.
Abbreviations
 ATE:

Average treatment effect
 ATT:

Average treatment effects on the treated
 ATU:

Average treatment effects on the untreated
 MSMs:

Marginal structural models
References
Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat. 2004;86:4–29.
Heckman JJ, Vytlacil E. PolicyRelevant Treatment Effects. Am Econ Rev. 2001;91:107–11.
Robins JM. Marginal Structural Models versus Structural nested Models as Tools for Causal inference. In: Halloran ME, Berry D, editors. Stat. Model. Epidemiol. Environ. Clin. Trials. New York: Springer; 2000. p. 95–133.
Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Math Model. 1986;7:1393–512.
Robins JM, Robins JM, Hernán MA, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–60.
Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JAC. Methods for dealing with timedependent confounding. Stat Med. 2013;32:1584–618.
Snowden JM, Rose S, Mortimer KM. Implementation of Gcomputation on a simulated data set: demonstration of a causal inference technique. Am J Epidemiol. 2011;173:731–8.
Keil AP, Edwards JK, Richardson DB, Naimi AI, Cole SR. The parametric gformula for timetoevent data: intuition and a worked example. Epidemiology. 2014;25:889–97.
Taubman SL, Robins JM, Mittleman MA, Hernán MA. Intervening on risk factors for coronary heart disease: an application of the parametric gformula. Int J Epidemiol. 2009;38:1599–611.
Danaei G, Pan A, Hu FB, Hernán MA. Hypothetical midlife interventions in women and risk of type 2 diabetes. Epidemiology. 2013;24:122–8.
Nianogo RA, Wang MC, Wang A, Nobari TZ, Crespi CM, Whaley SE, et al. Projecting the impact of hypothetical early life interventions on adiposity in children living in lowincome households. Pediatr Obes. 2016. doi:10.1111/ijpo.12157.
Cole SR, Frangakis CE. The consistency statement in causal inference: a definition or an assumption? Epidemiology. 2009;20:3–5.
Hernán MA, Robins JM. Estimating causal effects from epidemiological data. J Epidemiol Community Health. 2006;60:578–86.
Westreich D, Cole SR. Invited commentary: positivity in practice. Am J Epidemiol. 2010;171:674–7. discussion 678–81.
Efron B, Tibshirani R. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat Sci. 1986;1:54–75.
World Health Organization. WHO World Health Survey. World Health Organization;2016. Available: http://www.who.int/healthinfo/survey/en/. Accessed 1 June 2016.
Wang A, Stronks K, Arah OA. Global educational disparities in the associations between body mass index and diabetes mellitus in 49 lowincome and middleincome countries. J Epidemiol Community Health. 2014;68:705–11.
Moore KL, Neugebauer R, van der Laan MJ, Tager IB. Causal inference in epidemiological studies with strong confounding. Stat Med. 2012;31:1380–404.
Hernan MA, Robins JM. Standardization and the Parametric Gformula. Causal Inference. Chapman & Hall/CRC. 2015. Available from: http://www.hsph.harvard.edu/miguelhernan/causalinferencebook/. Accessed 1 June 2016.
Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61:962–72.
Vansteelandt S, Keiding N. Invited commentary: Gcomputationlost in translation? Am J Epidemiol. 2011;173:739–42.
Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology. 2003;14:680–6.
Acknowledgements
The authors benefited from facilities and resources provided by the California Center for Population Research at UCLA (CCPR), which receives core support (R24HD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).
Funding
AW was supported by the Dissertation Year Fellowship from the University of California, Los Angeles. RAN was supported by a Burroughs Wellcome Fellowship and the Dissertation Year Fellowship from UCLA. OAA was partly supported by grant R01HD07229601A1 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.
Availability of data and materials
The World Health Survey (WHS) is a large crosssectional study implemented by the WHO and conducted in 2002–2004 in 70 countries. The survey collected data on the health of adult populations and health systems using probabilistic sampling techniques. The study samples were nationally representative and included in total over 300,000 individuals aged 18 and older. WHS is publicly available at http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog (registration required). The SAS code is included in the supplementary file and the dataset for illustrative example is available upon request.
Authors’ contributions
AW and RAN participated in the study conception, design, analysis and interpretation of results and drafting of the article. OAA supervised the study and participated in study conception, design and interpretation of results, and made critical revisions to the paper for important intellectual content. All authors read and approved the final manuscript.
Competing interest
The authors declare that they have no competing interests.
Consent for publication
Not applicable as this is a secondary data analysis project.
Ethics approval and consent to participate
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Additional file
Additional file 1:
In the supplementary file, we presented two alternative ways for estimating ATT and ATU, and the detailed gcomputation steps, and the corresponding SAS code for the illustration. (DOCX 81 kb)
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Wang, A., Nianogo, R.A. & Arah, O.A. Gcomputation of average treatment effects on the treated and the untreated. BMC Med Res Methodol 17, 3 (2017). https://doi.org/10.1186/s1287401602824
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1287401602824