Instrumental Variable Estimation

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Instrumental variable estimation uses variables that shift treatment but affect outcomes only through treatment to recover causal effects from observational or experimental data. Classical two-stage and control-function methods are being extended with kernel, Gaussian-process, and neural approaches for nonlinear settings, including high-dimensional covariates, quantile effects, and changing environments. Researchers also use instruments in applications such as pricing, medical imaging, and treatment decisions, while Bayesian and conformal methods aim to quantify uncertainty or provide valid bounds. A central tension is that flexible estimators can handle complex data, but weak or invalid instruments and ill-posed inverse problems can make effects difficult to identify reliably.

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15 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS
Sep 23, 2026Spandan Ghose ChowdhuryCausal Effect Estimation Statistical Power Analysis
Georgia Institute of Technology, GA, USA
Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression
Aug 8, 2026Xingdong Feng, Xinhong Jiang, Yuling Jiao +2Quantile Regression Nonparametric Regression
School of Statistics and Data Science, Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai 200433, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · School of Artificial Intelligence, and Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, China +1
Graph Diffusion Residuals for Control-Function Instrumental Variables
Jun 12, 2026Rui Wu, Zongyuan Chen, Hong Xie +2Causal Effect Estimation Instrumental Variable Estimation
School of Computer Science and Engineering University of Science and Technology of China
Perturbative methods for non-parametric instrumental variable
May 29, 2026Wei Bu, Arthur GrettonKernel Ridge Regression Instrumental Variable Estimation
Harvard University, Northeastern University, Physics Department · University College London.
Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization
May 24, 2026JungHo Lee, Johnna Sundberg, Pim Welle +1Causal Effect Estimation Instrumental Variable Estimation
1Carnegie Mellon University · 2Allegheny County Department of Human Services
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
May 13, 2026Vahid Balazadeh, Hamidreza Kamkari, Medha Barath +2Causal Effect Estimation Parameter Identifiability
University of Toronto · Vector Institute · MIT CSAIL +1
BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates
May 7, 2026Guyue Luo, Haidong Lu, Andrew J. Loza +1Causal Effect Estimation Latent Variable Models
Department of Biostatistics Yale University
TabCF: Distributional Control Function Estimation with Tabular Foundation Models
May 7, 2026Geping Chen, Chunlin Li, Tianzhong Yang +2Causal Effect Estimation Instrumental Variable Estimation
Iowa State University · University of Virginia · University of Minnesota +1
Conformal Prediction for Nonparametric Instrumental Regression
Mar 26, 2026Masahiro KatoRegression Prediction Intervals Conformal Prediction
The University of Tokyo
CIV-DG: Conditional Instrumental Variables for Domain Generalization in Medical Imaging
Mar 26, 2026Shaojin Bai, Yuting Su, Weizhi NieDomain Generalization Cross-Domain Generalization
School of Electrical and Information Engineering, Tianjin University, Tianjin, China
What Do We Care About in Bandits with Noncompliance? BRACE: Bandits with Recommendations, Abstention, and Certified Effects
Mar 10, 2026Nicolás Della PennaContextual Bandits Instrumental Variable Estimation
Grouplang
OrthoFormer: Instrumental Variable Estimation in Transformer Hidden States via Neural Control Functions
Mar 8, 2026Charles LuoTransformer Instrumental Variable Estimation
Metro State University
Instrumental and Proximal Causal Inference with Gaussian Processes
Mar 2, 2026Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic +2Proximal Causal Inference Causal Inference
Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong · Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Germany · Australian Institute for Machine Learning, Adelaide University, Australia +1
Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
Feb 23, 2026Shimeng Huang, Matthew Robinson, Francesco LocatelloInstrumental Variable Estimation Causal Identifiability
Institute of Science and Technology Austria (ISTA)
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
Dec 28, 2025Rui Ai, David Simchi-Levi, Feng ZhuCausal Effect Estimation Instrumental Variable Estimation
MIT

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Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS

Spandan Ghose Chowdhury

Georgia Institute of Technology, GA, USA

Causal Effect Estimation Statistical Power Analysis Instrumental Variable Estimation Causal Inference

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Information systems (IS) researchers increasingly use quasi-experimental methods such as difference-in-differences (DiD) and instrumental variables (IV) to recover causal effects from observational panel data. Power calculations that justify these designs assume i.i.d. errors, but the deeper problem is what even a cluster-robust calculator cannot see. We report a Monte Carlo study over 9837 parameter conditions (approx 9.8 million datasets) and decompose the planned-versus-achieved power gap. The serial-correlation component is recoverable by an AR(1)-aware calculator when rho is known, and partially when rho must be estimated from short pre-periods, but panel attrition, staggered-adoption bias, and parallel-trends pretesting are captured by no closed-form formula; exogenous attrition alone costs approx 8 to 11 percentage points at the few-hundred-to-thousand sample sizes IS studies use. Treatment-correlated, outcome-dependent attrition instead induces bias, not just power loss. For IV, holding first-stage F fixed, larger N neither raises power nor curbs exclusion bias, though with a fixed instrument more data does sharpen the first stage, so identification rests on instrument strength, not sample size.