Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data

2013 ◽  
Vol 21 (2) ◽  
pp. 233-251 ◽  
Author(s):  
Walter R. Mebane ◽  
Paul Poast
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Treatment Effect ◽  
Bayesian Method ◽  
Treatment Effects ◽  
Applied Research ◽  
Average Treatment Effect ◽  
Potential Importance ◽  
Treatment Data

How a treatment causes a particular outcome is a focus of inquiry in political science. When treatment data are either nonrandomly assigned or missing, the analyst will often invoke ignorability assumptions: that is, both the treatment and missingness are assumed to be as if randomly assigned, perhaps conditional on a set of observed covariates. But what if these assumptions are wrong? What if the analyst does not know why—or even if—a particular subject received a treatment? Building on Manski, Molinari offers an approach for calculating nonparametric identification bounds for the average treatment effect of a binary treatment under general missingness or nonrandom assignment. To make these bounds substantively more informative, Molinari's technique permits adding monotonicity assumptions (e.g., assuming that treatment effects are weakly positive). Given the potential importance of these assumptions, we develop a new Bayesian method for performing sensitivity analysis regarding them. This sensitivity analysis allows analysts to interpret the assumptions' consequences quantitatively and visually. We apply this method to two problems in political science, highlighting the method's utility for applied research.

The Effect of Extension Services and Credit on Agricultural Production in Bolivia, Peru, and Colombia

2021 ◽  
Author(s):  
Mateus C. R. Neves ◽  
Felipe De Figueiredo Silva ◽  
Carlos Otávio Freitas
Keyword(s):  
Travel Time ◽  
Treatment Effect ◽  
Agricultural Production ◽  
Treatment Effects ◽  
Geographic Information ◽  
Average Treatment Effect ◽  
Extension Services ◽  
Average Treatment ◽  
Andean Countries ◽  
Average Treatment Effects

In this paper we estimate the average treatment effect from access to extension services and credit on agricultural production in selected Andean countries (Bolivia, Peru, and Colombia). More specifically, we want to identify the effect of accessibility, here represented as travel time to the nearest area with 1,500 or more inhabitants per square kilometer or at least 50,000 inhabitants, on the likelihood of accessing extension and credit. To estimate the treatment effect and identify the effect of accessibility on these variables, we use data from the Colombian and Bolivian Agricultural Censuses of 2013 and 2014, respectively; a national agricultural survey from 2017 for Peru; and geographic information on travel time. We find that the average treatment effect for extension is higher compared to that of credit for farms in Bolivia and Peru, and lower for Colombia. The average treatment effects of extension and credit for Peruvian farms are $2,387.45 and $3,583.42 respectively. The average treatment effect for extension and credit are $941.92 and $668.69, respectively, while in Colombia are $1,365.98 and $1,192.51, respectively. We also find that accessibility and the likelihood of accessing these services are nonlinearly related. Results indicate that higher likelihood is associated with lower travel time, especially in the analysis of credit.

scholarly journals Estimating Treatment Effects of Unit-Based Pricing of Household Solid Waste Disposal

2018 ◽  
Vol 48 (1) ◽  
pp. 21-43
Author(s):  
Christopher Wright ◽  
John M. Halstead ◽  
Ju-Chin Huang
Keyword(s):  
Propensity Score ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Propensity Score Matching ◽  
Waste Disposal ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Solid Waste Disposal ◽  
Annual Reduction

Propensity score matching is used to estimate treatment effects when data are observational. Results presented in this study demonstrate the use of propensity score matching to evaluate the average treatment effect of unit-based pricing of household trash for reducing municipal solid waste disposal. Average treatment effect of the treated for 34 New Hampshire communities range from an annual reduction of 631 pounds per household to 823 pounds per household. This represents an annual reduction of 42 percent to 54 percent from an average of 1530 pounds per household if a town did not adopt municipal solid waste user fees.

scholarly journals Treatment Interactions with Nonexperimental Data in Stata

2011 ◽  
Vol 11 (4) ◽  
pp. 545-555 ◽  
Author(s):  
Graham K. Brown ◽  
Thanos Mergoupis
Keyword(s):  
Experimental Data ◽  
Observational Data ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Interaction Parameters ◽  
Average Treatment ◽  
Interaction Terms ◽  
Treatment Interaction ◽  
Predicted Values

Treatment effects may vary with the observed characteristics of the treated, often with important implications. In the context of experimental data, a growing literature deals with the problem of specifying treatment interaction terms that most effectively capture this variation. Some results of this literature are now implemented in Stata. With nonexperimental (observational) data, and in particular when selection into treatment depends on unmeasured factors, treatment effects can be estimated using Stata's treatreg command. Though not originally designed for this purpose, treatreg can be used to consistently estimate treatment interaction parameters. With interactions, however, adjustments are required to generate predicted values and estimate the average treatment effect. In this article, we introduce commands that perform this adjustment for multiplicative interactions, and we show the required adjustment for more complicated interactions.

scholarly journals Bootstrap bias correction for average treatment effects with inverse propensity weights

2019 ◽  
Vol 52 (2) ◽  
pp. 187-200
Author(s):  
GUBHINDER KUNDHI ◽  
MARCEL VOIA
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Bias Correction ◽  
Treatment Effects ◽  
Potential Problem ◽  
Average Treatment Effect ◽  
Omitted Variables ◽  
Control Groups ◽  
Average Treatment ◽  
And Control

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

scholarly journals A Selection Bias Approach to Sensitivity Analysis for Causal Effects

2014 ◽  
Vol 22 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Matthew Blackwell
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Selection Bias ◽  
Large Volume ◽  
Quantitative Approach ◽  
Causal Effects ◽  
Omitted Variables ◽  
Fundamental Fact ◽  
Unmeasured Confounders

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

Abstract P106: Population-Based Drug Repurposing to Treat Thrombosis in COVID-19 Patients

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Yejin Kim ◽  
Luyao Chen ◽  
Xiaoqian Jiang ◽  
Sean I Savitz
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Significant Treatment ◽  
Treatment Conditions ◽  
Increased Risk ◽  
Comparable Control ◽  
Admission Status ◽  
Pre Treatment

Introduction: Patients with coronavirus disease 2019 (COVID-19) have an increased risk of thrombosis. Our objective is to obtain population-level treatment effects of drugs on treating thrombosis in COVID-19. Methods: We conducted a retrospective analysis of Optum electronic health records (EHRs) with 34,043 hospitalized COVID-19 patients. We identified case-patient with thrombosis (stroke, deep vein thrombosis, pulmonary embolism, and myocardial infarction) using PheWas codes. The propensity score matching was used to select comparable control patients who survived without any thrombosis based on demographics and admission status (temperature and SpO 2 level). We computed the average treatment effect (ATT) for medication using advanced inverse propensity score weighting based on pre-treatment conditions (i.e., comorbidities in the last 6 months and medications in the last 2 months before hospitalization). Results: We identified 2,446 case-patients with thrombosis and 5,020 comparable control patients. There were a total of 540 drugs that were administered in at least 80 patients. We calculated the 540 drugs’ ATT coefficient. As a result, 23 drugs had a positive ATT coefficient with a p -value of less than 0.05. After filtering out commonly prescribed symptomatic drugs (e.g., Acetaminophen, Guaifenesin, and Ondansetron), we highlight the following drugs with statistically significant treatment effects: Atorvastatin (ATT=0.34), Ceftriaxone (ATT=0.26), Levothyroxine (ATT=0.26), Albuterol (ATT=0.25), Azithromycin (ATT=0.23), Enoxaparin (ATT=0.20), and Metformin (ATT=0.20). Conclusions: In this preliminary work, we identified anti-thrombotic drugs (Enoxaparin) but also anti-inflammatory drugs (Atorvastatin, Metformin) and possibly antibiotics that have a significant treatment effect in COVID-19 patients that could reduce risk of thrombosis. We also observed that several anti-thrombotic drugs (Apixaban and Ticagrelor) had negative treatment effects, which was partly due to an imbalance in pre-treatment conditions. Our future work is to incorporate more extensive data (such as lab tests and vital signs) into the propensity scores to better capture the severity of admission status.

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