scholarly journals Using Instrumental Variables for Inference about Policy Relevant Treatment Effects

2017 ◽  
Author(s):  
Magne Mogstad ◽  
Andres Santos ◽  
Alexander Torgovitsky
Keyword(s):  
Instrumental Variables ◽  
Treatment Effects

Use of instrumental variables in electronic health record-driven models

2016 ◽  
Vol 27 (2) ◽  
pp. 608-621 ◽  
Author(s):  
Luca Salmasi ◽  
Enrico Capobianco
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Instrumental Variables ◽  
Treatment Effects ◽  
Ad Hoc ◽  
Health Record ◽  
Health Records ◽  
Negative Effect ◽  
Electronic Health ◽  
High Degree

Precision medicine presents various methodological challenges whose assessment requires the consideration of multiple factors. In particular, the data multitude in the Electronic Health Records poses interoperability issues and requires novel inference strategies. A problem, though apparently a paradox, is that highly specific treatments and a variety of outcomes may hardly match with consistent observations (i.e., large samples). Why is it the case? Owing to the heterogeneity of Electronic Health Records, models for the evaluation of treatment effects need to be selected, and in some cases, the use of instrumental variables might be necessary. We studied the recently defined person-centered treatment effects in cancer and C-section contexts from Electronic Health Record sources and identified as an instrument the distance of patients from hospitals. We present first the rationale for using such instrument and then its model implementation. While for cancer patients consideration of distance turns out to be a penalty, implying a negative effect on the probability of receiving surgery, a positive effect is instead found in C-section due to higher propensity of scheduling delivery. Overall, the estimated person-centered treatment effects reveal a high degree of heterogeneity, whose interpretation remains context-dependent. With regard to the use of instruments in light of our two case studies, our suggestion is that this process requires ad hoc variable selection for both covariates and instruments and additional testing to ensure validity.

Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction

2021 ◽  
Author(s):  
Martin E Andresen ◽  
Martin Huber
Keyword(s):  
Instrumental Variables ◽  
Treatment Effects ◽  
College Graduation ◽  
Average Effect ◽  
Exclusion Restriction ◽  
Unit Level ◽  
Policy Relevance ◽  
Induced Changes ◽  
Local Average

Summary When estimating local average and marginal treatment effects using instrumental variables (IVs), multivalued endogenous treatments are frequently converted to binary measures, supposedly to improve interpretability or policy relevance. Such binarisation introduces a violation of the IV exclusion if (a) the IV affects the multivalued treatment within support areas below and/or above the threshold and (b) such IV-induced changes in the multivalued treatment affect the outcome. We discuss assumptions that satisfy the IV exclusion restriction with a binarised treatment and permit identifying the average effect of (a) the binarised treatment and (b) unit-level increases in the original multivalued treatment among specific compliers. We derive testable implications of these assumptions and propose tests which we apply to the estimation of the returns to college graduation instrumented by college proximity.

INSTRUMENTAL VARIABLES METHODS WITH HETEROGENEITY AND MISMEASURED INSTRUMENTS

2016 ◽  
Vol 33 (1) ◽  
pp. 69-104 ◽  
Author(s):  
Karim Chalak
Keyword(s):  
Discrete Choice ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Missing Observations ◽  
Structural System ◽  
Necessary And Sufficient Condition ◽  
Weighted Averages ◽  
Average Treatment Effects ◽  
Necessary And Sufficient

We study the consequences of substituting an error-laden proxy W for an instrument Z on the interpretation of Wald, local instrumental variable (LIV), and instrumental variable (IV) estimands in an ordered discrete choice structural system with heterogeneity. A proxy W need only satisfy an exclusion restriction and that the treatment and outcome are mean independent from W given Z. Unlike Z, W need not satisfy monotonicity and may, under particular specifications, fail exogeneity. For example, W could code Z with error, with missing observations, or coarsely. We show that Wald, LIV, and IV estimands using W identify weighted averages of local or marginal treatment effects (LATEs or MTEs). We study a necessary and sufficient condition for nonnegative weights. Further, we study a condition under which the Wald or LIV estimand using W identifies the same LATE or MTE that would have been recovered had Z been observed. For example, this holds for binary Z and therefore the Wald estimand using W identifies the same “average causal response,” or LATE for binary treatment, that would have been recovered using Z. Also, under this condition, LIV using W can be used to identify MTE and average treatment effects for e.g., the population, treated, and untreated.

Instrumental Variables for Binary Treatments with Heterogenous Treatment Effects: A Simple Exposition

2004 ◽  
Vol 3 (1) ◽  
Author(s):  
Alan Manning
Keyword(s):  
Instrumental Variables ◽  
Treatment Effects ◽  
Functional Form ◽  
Treatment Variable ◽  
Distributional Assumptions ◽  
Simple Exposition

Abstract This note provides a simple exposition of what IV can and cannot estimate in a model with binary treatment variable and heterogenous treatment effects. It shows how linear IV is essentially a misspecification of functional form and the reason why linear IV estimates will generally depend on the instrument used is because of this misspecification. It shows that if one can estimate the correct functional form then the treatment effects are independent of the instruments used. However, the data may not be rich enough in practice to be able to identify these treatments effects without strong distributional assumptions. In this case, one will have to settle for estimations of treatment effects that are instrument-dependent.

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