55 citations · 110 across the 11 of their papers we have counts for
11 papers
M-estimation under Two-Phase Multiwave Sampling with Applications to Prediction-Powered Inference
Dan M. Kluger, Stephen Bates
In two-phase multiwave sampling, inexpensive measurements are collected on a large sample and expensive, more informative measurements are adaptively obtained on subsets of units a…
Conformal Prediction Sets for Instance Segmentation
Kerri Lu, Dan M. Kluger, Stephen Bates +1
Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is…
Demystifying Prediction Powered Inference
Yilin Song, Dan M. Kluger, Harsh Parikh +1
Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social s…
Precrop Payoffs: Causal machine learning reveals large but variable yield benefits of crop rotation in major breadbaskets
Dan M. Kluger, Stefania Di Tommaso, David B. Lobell
Building sustainable food systems that are resilient to climate change will require improved agricultural management and policy. One common practice that is well-known to benefit c…
Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling
Dan M. Kluger, Kerri Lu, Tijana Zrnic +2
Machine learning models are increasingly used to produce predictions that serve as input data in subsequent statistical analyses. For example, computer vision predictions of econom…
Regression coefficient estimation from remote sensing maps
Kerri Lu, Dan M. Kluger, Stephen Bates +1
Regressions are commonly used in environmental science and economics to identify causal or associative relationships between variables. In these settings, remote sensing-derived ma…
Biases in estimates of air pollution impacts: the role of omitted variables and measurement errors
Dan M. Kluger, David B. Lobell, Art B. Owen
Observational studies often use linear regression to assess the effect of ambient air pollution on outcomes of interest, such as human health indicators or crop yields. Yet polluti…
Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields
Dan M. Kluger, Art B. Owen, David B. Lobell
With climate change threatening agricultural productivity and global food demand increasing, it is important to better understand which farm management practices will maximize crop…
Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions
Dan M. Kluger, Sherrie Wang, David B. Lobell
Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw…
A central limit theorem for the Benjamini-Hochberg false discovery proportion under a factor model
Dan M. Kluger, Art B. Owen
The Benjamini-Hochberg (BH) procedure remains widely popular despite having limited theoretical guarantees in the commonly encountered scenario of correlated test statistics. Of pa…
Kernel regression analysis of tie-breaker designs
Dan M. Kluger, Art B. Owen
Tie-breaker experimental designs are hybrids of Randomized Controlled Trials (RCTs) and Regression Discontinuity Designs (RDDs) in which subjects with moderate scores are placed in…