scholarly journals Policy Learning With Observational Data

Econometrica ◽  
2021 ◽  
Vol 89 (1) ◽  
pp. 133-161 ◽  
Author(s):  
Susan Athey ◽  
Stefan Wager
Keyword(s):  
Observational Data ◽  
Functional Form ◽  
Causal Effect ◽  
Individual Characteristics ◽  
Efficient Estimation ◽  
Causal Effects ◽  
Robust Estimator ◽  
New Approach ◽  
Doubly Robust ◽  
Application Specific

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

scholarly journals Doubly Robust Estimation of Causal Effect

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiaochun Li ◽  
Changyu Shen
Keyword(s):  
Propensity Score ◽  
Causal Effect ◽  
Model Specification ◽  
Robust Estimator ◽  
Data Set ◽  
Multiple Regressions ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Confounding Adjustment ◽  
The Relationship

Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.

You Can’t Drive a Car With Only Three Wheels

2019 ◽  
Vol 188 (9) ◽  
pp. 1682-1685 ◽  
Author(s):  
Hailey R Banack
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Causal Effects ◽  
Measurement Bias ◽  
Exposure Misclassification ◽  
Unmeasured Confounding ◽  
Obstetrical Care ◽  
The Impact ◽  
Fundamental Requirement

Abstract Authors aiming to estimate causal effects from observational data frequently discuss 3 fundamental identifiability assumptions for causal inference: exchangeability, consistency, and positivity. However, too often, studies fail to acknowledge the importance of measurement bias in causal inference. In the presence of measurement bias, the aforementioned identifiability conditions are not sufficient to estimate a causal effect. The most fundamental requirement for estimating a causal effect is knowing who is truly exposed and unexposed. In this issue of the Journal, Caniglia et al. (Am J Epidemiol. 2019;000(00):000–000) present a thorough discussion of methodological challenges when estimating causal effects in the context of research on distance to obstetrical care. Their article highlights empirical strategies for examining nonexchangeability due to unmeasured confounding and selection bias and potential violations of the consistency assumption. In addition to the important considerations outlined by Caniglia et al., authors interested in estimating causal effects from observational data should also consider implementing quantitative strategies to examine the impact of misclassification. The objective of this commentary is to emphasize that you can’t drive a car with only three wheels, and you also cannot estimate a causal effect in the presence of exposure misclassification bias.

scholarly journals Interpretation of mendelian randomization using one measure of an exposure that varies over time.

2021 ◽  
Author(s):  
Tim T Morris ◽  
Jon Heron ◽  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Kate Tilling
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Observational Data ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Time Points ◽  
The Life Course ◽  
Over Time

Background Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time. Methods We propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example. Results We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure. Conclusions Practitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.

scholarly journals A Weighted Covariate Balancing Method of Estimating Causal Effects in Case-Control Studies

2019 ◽  
Vol 13 (4) ◽  
pp. 40
Author(s):  
Lateef B. Amusa ◽  
Temesgen Zewotir ◽  
Delia North
Keyword(s):  
Observational Data ◽  
Mahalanobis Distance ◽  
Treatment Effects ◽  
Bias Reduction ◽  
Efficient Estimation ◽  
Causal Effects ◽  
Case Control Studies ◽  
Model Framework ◽  
Weighting Method ◽  
Distance Method

Propensity score methods have dominated the estimation of treatment effects based on observational data and particularly in the health and medical sciences. We propose a weighting method based on rank-based Mahalanobis distance, namely the covariate balancing rank-based Mahalanobis distance method, to estimate causal effects for observational data. Using Monte Carlo simulations, under different data structures and type of outcome variables, the proposed method is shown to have better performance, in terms of bias reduction and treatment effect estimation. Specifically, under the generalized linear model framework, we simulated datasets based on the Lalonde-PSID study, for linear link function; while datasets were simulated based on the Lindner study, for non-linear link functions. We further apply the proposed method to data extracted from the Nigeria Demographic Health Survey (2013), to investigate the effect of educational exposure on ideal family size among married couples in Nigeria. The proposed method is a viable alternative method that can improve covariates balance, bias reduction, and efficient estimation of treatment effects.

scholarly journals Marginal Structural Models with Counterfactual Effect Modifiers

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Wenjing Zheng ◽  
Zhehui Luo ◽  
Mark J van der Laan
Keyword(s):  
Maximum Likelihood ◽  
Causal Inference ◽  
Influence Function ◽  
Structural Model ◽  
Causal Effect ◽  
Structural Models ◽  
Efficient Estimation ◽  
Robust Estimator ◽  
Marginal Structural Models ◽  
Targeted Maximum Likelihood

Abstract In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers are also of interest. In this work, we investigate the robust and efficient estimation of the Counterfactual-History-Adjusted Marginal Structural Model (van der Laan MJ, Petersen M. Statistical learning of origin-specific statically optimal individualized treatment rules. Int J Biostat. 2007;3), which models the conditional intervention-specific mean outcome given a counterfactual modifier history in an ideal experiment. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2, van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data, 1st ed. Springer Series in Statistics. Springer, 2011). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence function (and the corresponding projected TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence function becomes taxing. We compare the projected TMLE estimator with an Inverse Probability of Treatment Weighted estimator (e.g. Robins JM. Marginal structural models. In: Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1-10. 1997a, Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000;11:561–570), and a non-targeted G-computation estimator (Robins JM. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393–1512.). The comparative performance of these estimators is assessed in a simulation study. The use of the projected TMLE estimator is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial where effect modifiers are subject to missing at random.

scholarly journals Adversarial balancing-based representation learning for causal effect inference with observational data

2021 ◽  
Author(s):  
Xin Du ◽  
Lei Sun ◽  
Wouter Duivesteijn ◽  
Alexander Nikolaev ◽  
Mykola Pechenizkiy
Keyword(s):  
Health Care ◽  
Observational Data ◽  
Causal Effect ◽  
Representation Learning ◽  
Average Treatment Effect ◽  
Treatment Selection ◽  
Causal Effects ◽  
Control Group ◽  
Confounding Bias ◽  
The Impact

AbstractLearning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve the survival rate. In this paper, we focus on studying the problem of estimating the Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, in the presence of confounding bias; on the other hand, we have to deal with the identification of the CATE when the distributions of covariates over the treatment group units and the control units are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning. To ensure the identification of the CATE, ABCEI uses adversarial learning to balance the distributions of covariates in the treatment and the control group in the latent representation space, without any assumptions on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, encompassing several health care (and other) domains.

Using Propensity Score Matching to Improve Validity in Public Administration Research

2022 ◽  
pp. 45-60
Author(s):  
Michael Howell-Moroney
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Public Administration ◽  
Propensity Score Matching ◽  
Observational Data ◽  
Randomized Clinical Trials ◽  
Causal Effect ◽  
Social Science Research ◽  
Causal Effects ◽  
Government Employment

Randomized clinical trials have a longstanding status as the gold standard in detecting causal effects. In the social sciences, randomized clinical trials are rare because of their attendant logistical and cost burdens. Most social science research makes use of observational data. The empirical challenge posed by observational data is that treatment assignment is no longer random. This challenge continues to spur innovation across many disciplines toward more sophisticated techniques for estimating causal relationships. Scholars have developed a common theoretical framework for estimating causal effects, often called the potential outcomes or counterfactual framework. This chapter demonstrates the propensity score matching methodology as a way to estimate causal effects using observational data. Throughout, an example from public administration research, the effect of government employment on volunteerism, is used to illustrate the concepts. Empirical estimates of the treatment effects show that there may be a causal effect of government employment on volunteerism.

scholarly journals Understanding the nature of association between anxiety phenotypes and anorexia nervosa: a triangulation approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
E. Caitlin Lloyd ◽  
Hannah M. Sallis ◽  
Bas Verplanken ◽  
Anne M. Haase ◽  
Marcus R. Munafò
Keyword(s):  
Anorexia Nervosa ◽  
Longitudinal Study ◽  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
Causal Effect ◽  
Causal Effects ◽  
Observational Research ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Depressed Affect

Abstract Background Evidence from observational studies suggests an association between anxiety disorders and anorexia nervosa (AN), but causal inference is complicated by the potential for confounding in these studies. We triangulate evidence across a longitudinal study and a Mendelian randomization (MR) study, to evaluate whether there is support for anxiety disorder phenotypes exerting a causal effect on AN risk. Methods Study One assessed longitudinal associations of childhood worry and anxiety disorders with lifetime AN in the Avon Longitudinal Study of Parents and Children cohort. Study Two used two-sample MR to evaluate: causal effects of worry, and genetic liability to anxiety disorders, on AN risk; causal effects of genetic liability to AN on anxiety outcomes; and the causal influence of worry on anxiety disorder development. The independence of effects of worry, relative to depressed affect, on AN and anxiety disorder outcomes, was explored using multivariable MR. Analyses were completed using summary statistics from recent genome-wide association studies. Results Study One did not support an association between worry and subsequent AN, but there was strong evidence for anxiety disorders predicting increased risk of AN. Study Two outcomes supported worry causally increasing AN risk, but did not support a causal effect of anxiety disorders on AN development, or of AN on anxiety disorders/worry. Findings also indicated that worry causally influences anxiety disorder development. Multivariable analysis estimates suggested the influence of worry on both AN and anxiety disorders was independent of depressed affect. Conclusions Overall our results provide mixed evidence regarding the causal role of anxiety exposures in AN aetiology. The inconsistency between outcomes of Studies One and Two may be explained by limitations surrounding worry assessment in Study One, confounding of the anxiety disorder and AN association in observational research, and low power in MR analyses probing causal effects of genetic liability to anxiety disorders. The evidence for worry acting as a causal risk factor for anxiety disorders and AN supports targeting worry for prevention of both outcomes. Further research should clarify how a tendency to worry translates into AN risk, and whether anxiety disorder pathology exerts any causal effect on AN.

scholarly journals The Pice Effects of Competition from Parallel Imports and Therapeutic Alternatives: Using Dynamic Models to Estimate the Causal Effect on the Extensive and Intensive Margins

2021 ◽  
Author(s):  
David Granlund
Keyword(s):  
Dynamic Models ◽  
Causal Effect ◽  
The Other ◽  
Causal Effects ◽  
Parallel Imports ◽  
Extensive And Intensive Margins ◽  
Price Effects ◽  
Short And Long Term ◽  
Therapeutic Alternatives

AbstractThis paper studies responses to competition with the use of dynamic models that distinguish between short- and long-term price effects. The dynamic models also allow lagged numbers of competitors to become valid and strong instruments for the current numbers, which enables studying the causal effects using flexible specifications. A first parallel trader is found to decrease prices of exchangeable products by 7% in the long term. On the other hand, prices do not respond to the first competitor that sells therapeutic alternatives; but competition from four or more competitors that sell on-patent therapeutic alternatives decreases prices by about 10% in the long term.

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