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Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables

arXiv 1611.09925 ↗First posted 29 Nov 2016stat.MEcited by 23

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Abstract (arXiv)

Instrumental variables (IVs) are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard IV model, however, the average treatment effect (ATE) is only partially identifiable. To address this, we propose novel assumptions that allow for identification of the ATE. Our identification assumptions are clearly separated from model assumptions needed for estimation, so that researchers are not required to commit to a specific observed data model in establishing identification. We then construct multiple estimators that are consistent under three different observed data models, and multiply robust estimators that are consistent in the union of these observed data models. We pay special attention to the case of binary outcomes, for which we obtain bounded estimators of the ATE that are guaranteed to lie between -1 and 1. Our approaches are illustrated with simulations and a data analysis evaluating the causal effect of education on earnings.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Nov 2016.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 6; 2024: 4; 2025: 3; 2026: 1.

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  1. 2021Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confoundingcited by 21
  2. 2022An Instrumental Variable Approach to Confounded Off-Policy Evaluationcited by 20
  3. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 18
  4. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  5. 2021Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variablecited by 13
  6. 2025Doubly Robust Alignment for Large Language Modelscited by 12
  7. 2022Instrumental Variables in Causal Inference and Machine Learning: A Surveycited by 10
  8. 2021Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradoxcited by 8
  9. 2023Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimizationcited by 7
  10. 2023Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharingcited by 7
  11. 2024Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameterscited by 5
  12. 2024Targeted Sequential Indirect Experiment Designcited by 4
11 more
  1. 2024Learning Representations of Instruments for Partial Identification of Treatment Effectscited by 4
  2. 2022RISE: Robust Individualized Decision Learning with Sensitive Variablescited by 3
  3. 2023Learning with Selectively Labeled Data from Multiple Decision-makerscited by 3
  4. 2022Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventionscited by 2
  5. 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Datacited by 2
  6. 2025Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combinationcited by 2
  7. 2025Efficient Adaptive Experimentation with Noncompliancecited by 2
  8. 2023Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learningcited by 1
  9. 2022Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Informationcited by 0
  10. 2023A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learningcited by 0
  11. 2026What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Viewscited by 0