scholarly journals On the Plausibility of the Latent Ignorability Assumption

Econometrics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 47
Author(s):  
Martin Huber
Keyword(s):  
Experimental Study ◽  
Instrumental Variable ◽  
Causal Effect ◽  
Sample Selection ◽  
Treatment Compliance ◽  
Job Corps ◽  
Program Effect ◽  
Empirical Illustration

The estimation of the causal effect of an endogenous treatment based on an instrumental variable (IV) is often complicated by the non-observability of the outcome of interest due to attrition, sample selection, or survey non-response. To tackle the latter problem, the latent ignorability (LI) assumption imposes that attrition/sample selection is independent of the outcome conditional on the treatment compliance type (i.e., how the treatment behaves as a function of the instrument), the instrument, and possibly further observed covariates. As a word of caution, this note formally discusses the strong behavioral implications of LI in rather standard IV models. We also provide an empirical illustration based on the Job Corps experimental study, in which the sensitivity of the estimated program effect to LI and alternative assumptions about outcome attrition is investigated.

scholarly journals Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation

2020 ◽  
Vol 102 (2) ◽  
pp. 355-367
Author(s):  
Gerard J. van den Berg ◽  
Petyo Bonev ◽  
Enno Mammen
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Asymptotic Properties ◽  
Right Censoring ◽  
Unemployment Duration ◽  
Dynamic Selection ◽  
Average Causal Effect ◽  
Variable Approach ◽  
Dynamic Treatment ◽  
Job Finding

We develop an instrumental variable approach for identification of dynamic treatment effects on survival outcomes in the presence of dynamic selection, noncompliance, and right-censoring. The approach is nonparametric and does not require independence of observed and unobserved characteristics or separability assumptions. We propose estimation procedures and derive asymptotic properties. We apply our approach to evaluate a policy reform in which the pathway of unemployment benefits as a function of the unemployment duration is modified. Those who were unemployed at the reform date could choose between the old and the new regime. We find that the new regime has a positive average causal effect on the job finding rate.

scholarly journals Psychological treatments for early psychosis can be beneficial or harmful, depending on the therapeutic alliance: an instrumental variable analysis

2015 ◽  
Vol 45 (11) ◽  
pp. 2365-2373 ◽  
Author(s):  
L. P. Goldsmith ◽  
S. W. Lewis ◽  
G. Dunn ◽  
R. P. Bentall
Keyword(s):  
Therapeutic Alliance ◽  
Effect Size ◽  
Instrumental Variable ◽  
Structural Equation ◽  
Psychological Treatment ◽  
Causal Effect ◽  
Controlled Trial ◽  
Behavioural Therapy ◽  
Psychological Treatments ◽  
Symptomatic Outcome

BackgroundThe quality of the therapeutic alliance (TA) has been invoked to explain the equal effectiveness of different psychotherapies, but prior research is correlational, and does not address the possibility that individuals who form good alliances may have good outcomes without therapy.MethodWe evaluated the causal effect of TA using instrumental variable (structural equation) modelling on data from a three-arm, randomized controlled trial of 308 people in an acute first or second episode of a non-affective psychosis. The trial compared cognitive behavioural therapy (CBT) over 6 weeks plus routine care (RC) v. supportive counselling (SC) plus RC v. RC alone. We examined the effect of TA, as measured by the client-rated CALPAS, on the primary trial 18-month outcome of symptom severity (PANSS), which was assessed blind to treatment allocation.ResultsBoth adjunctive CBT and SC improved 18-month outcomes, compared to RC. We showed that, for both psychological treatments, improving TA improves symptomatic outcome. With a good TA, attending more sessions causes a significantly better outcome on PANSS total score [effect size −2.91, 95% confidence interval (CI) −0.90 to −4.91]. With a poor TA, attending more sessions is detrimental (effect size +7.74, 95% CI +1.03 to +14.45).ConclusionsThis is the first ever demonstration that TA has a causal effect on symptomatic outcome of a psychological treatment, and that poor TA is actively detrimental. These effects may extend to other therapeutic modalities and disorders.

scholarly journals Early-life Medicaid Coverage and Intergenerational Economic Mobility

2018 ◽  
Vol 59 (2) ◽  
pp. 300-315 ◽  
Author(s):  
Rourke L. O’Brien ◽  
Cassandra L. Robertson
Keyword(s):  
Low Income ◽  
Early Life ◽  
Instrumental Variable ◽  
Regression Models ◽  
Causal Effect ◽  
Economic Mobility ◽  
Medicaid Coverage ◽  
Instrumental Variable Regression ◽  
Normative Evaluation ◽  
Medicaid Eligibility

New data reveal significant variation in economic mobility outcomes across U.S. localities. This suggests that social structures, institutions, and public policies—particularly those that influence critical early-life environments—play an important role in shaping mobility processes. Using new county-level estimates of intergenerational economic mobility for children born between 1980 and 1986, we exploit the uneven expansions of Medicaid eligibility across states to isolate the causal effect of this specific policy change on mobility outcomes. Instrumental-variable regression models reveal that increasing the proportion of low-income pregnant women eligible for Medicaid improved the mobility outcomes of their children in adulthood. We find no evidence that Medicaid coverage in later childhood years influences mobility outcomes. This study has implications for the normative evaluation of this policy intervention as well as our understanding of mobility processes in an era of rising inequality.

Does Intra-household Contagion Cause an Increase in Prescription Opioid Use?

2019 ◽  
Vol 84 (4) ◽  
pp. 577-608 ◽  
Author(s):  
Mathijs de Vaan ◽  
Toby Stuart
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Prescription Opioid ◽  
Assortative Matching ◽  
Opioid Use ◽  
Medical Claims ◽  
The Family ◽  
Physician Networks ◽  
Prescription Opioid Use ◽  
Prescription Behavior

Opioid use claims many thousands of lives each year. This article considers the diffusion of prescription opioid (PO) use within family households as one potential culprit of the proliferation of these medications. In an analysis of hundreds of millions of medical claims and almost 14 million opioid prescriptions in one state between 2010 and 2015, we show that the use of POs spreads within family households. We also show that the treatment effect of exposure to a family member’s PO use is driven by an increase in PO consumption for medical conditions that members of treated and untreated families experience at nearly identical rates. This pattern of results suggests household exposure causes an uptick in patient demand for prescription opioids. We use an instrumental variable estimation strategy to address the well-known challenges to estimating a causal effect of intra-household contagion, such as genotypic similarities among family members, assortative matching in partner selection, and clustering of health conditions within households. The results spotlight the salience of the most ubiquitous social structure, the family household, in accelerating opioid consumption to unprecedented levels. The findings also suggest that rather than direct social influence between physicians, the spread of prescription behavior in physician networks may be driven by shifts in patient demand that propagate through the patient sharing network.

Instrumental variable estimation of the marginal structural Cox model for time-varying treatments

Biometrika ◽  
2021 ◽  
Author(s):  
Y Cui ◽  
H Michael ◽  
F Tanser ◽  
E Tchetgen Tchetgen
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Cox Model ◽  
Sufficient Conditions ◽  
Structural Models ◽  
Model Parameters ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Unmeasured Confounding ◽  
Sequential Randomization

Summary Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural models to evaluate the causal effect of time-varying treatments on a censored failure time outcome. In this paper, we establish sufficient conditions for identification of marginal structural Cox model parameters with the aid of a time-varying instrumental variable, when sequential randomization fails to hold due to unmeasured confounding. Our instrumental variable identification condition rules out any interaction between an unmeasured confounder and the instrumental variable in its additive effects on the treatment process, the longitudinal generalization of the identifying condition of Wang & Tchetgen Tchetgen (2018). We describe a large class of weighted estimating equations that give rise to consistent and asymptotically normal estimators of the marginal structural Cox model, thereby extending the standard inverse probability of treatment weighted estimation of marginal structural models to the instrumental variable setting. Our approach is illustrated via extensive simulation studies and an application to estimate the effect of community antiretroviral therapy coverage on HIV incidence.

A National Multi-City Instrumental Variable Analysis of the Causal Effect of Local Particles on Mortality

2018 ◽  
Vol 2017 (1) ◽  
pp. 973
Author(s):  
Joel Schwartz ◽  
Antonella Zanobetti ◽  
Kelvin Fong ◽  
Petros Koutrakis
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Instrumental Variable Analysis ◽  
Variable Analysis

scholarly journals Do Sports Crowd Out Books? The Impact of Intercollegiate Athletic Participation on Grades

2017 ◽  
Vol 20 (1) ◽  
pp. 115-153 ◽  
Author(s):  
Michael A. Insler ◽  
Jimmy Karam
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Peer Groups ◽  
Sports Participation ◽  
Athletic Participation ◽  
Naval Academy ◽  
Intercollegiate Athletic ◽  
Using Data ◽  
Promotion Rates ◽  
The Impact

We investigate the influence of intercollegiate athletic participation on grades using data from the U.S. Naval Academy. Athletic participation is an endogenous decision with respect to educational outcomes. To identify a causal effect, we develop an instrument via the Academy’s random assignment of students into peer groups. Instrumental variable (IVs) estimates suggest that sports participation modestly reduces recruited athletes’ grades. This finding has implications beyond college, as we also show that grades—not athletic participation—are most strongly associated with postcollegiate outcomes such as military tenure and promotion rates.

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