• Corpus ID: 236956405

Improving Inference from Simple Instruments through Compliance Estimation

  title={Improving Inference from Simple Instruments through Compliance Estimation},
  author={Stephen Coussens and Jann Spiess},
Instrumental variables (IV) regression is widely used to estimate causal treatment effects in settings where receipt of treatment is not fully random, but there exists an instrument that generates exogenous variation in treatment exposure. While IV can recover consistent treatment effect estimates, they are often noisy. Building upon earlier work in biostatistics (Joffe and Brensinger, 2003) and relating to an evolving literature in econometrics (including Abadie et al., 2019; Huntington-Klein… 

Figures and Tables from this paper

Data-driven exclusion criteria for instrumental variable studies
This study frames exclusion as a data-driven estimation problem, and applies flexible machine learning methods to estimate the probability of a unit complying with the instrument, and demonstrates how excluding likely noncompliers can increase power while maintaining valid treatment effect estimates.
Variance Reduction for Experiments with One-Sided Triggering using CUPED
In online experimentation, trigger-dilute analysis is an approach to obtain more precise estimates of intent-to-treat (ITT) effects when the intervention is only exposed, or "triggered", for a small


Identification of Causal Effects Using Instrumental Variables
It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
We propose a method for using instrumental variables (IV) to draw inference about causal effects for individuals other than those affected by the instrument at hand. The question of policy relevance
Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness
Abstract In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect
Instrumental Variable Estimation with First-Stage Heterogeneity
We propose a simple data-driven procedure that exploits heterogeneity in the first stage correlation between an instrument and an endogenous variable to improve the efficiency of instrumental
Non-Random Exposure to Exogenous Shocks: Theory and Applications
We develop new tools for causal inference in settings where exogenous shocks affect the treatment status of multiple observations jointly, to different extents. In these settings researchers may
Sharp instruments for classifying compliers and generalizing causal effects
A new measure of IV quality called "sharpness" is introduced, which reflects the variation in compliance explained by covariates, and captures how well one can identify compliers and obtain tight bounds on identifiable subgroup effects.
Instrumental variables estimation with many weak instruments using regularized JIVE
Policy Evaluation with Multiple Instrumental Variables
Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity
Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
A fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates is provided and its asymptotic validity under non-Gaussian, heteroscedastic disturbances is established.
Instrumental Variables Regression with Weak Instruments
This paper develops asymptotic distribution theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here