Search Results for author:
Found 22 papers, 12 papers with code
Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series
We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides approximately calibrated prediction intervals.
Estimating Counterfactual Matrix Means with Short Panel Data
We develop a more flexible approach for identifying and estimating average counterfactual outcomes when several but not all possible outcomes are observed for each unit in a large cross section.
Causal clustering: design of cluster experiments under network interference
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers.
Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
Finally, we propose an efficient computational framework, enabling implementation on many practical problems in causal inference.
Policy Learning under Biased Sample Selection
Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population.
What Estimators Are Unbiased For Linear Models?
Despite the elegant proof, it was shown by the authors and other researchers that the main result in the earlier version of Hansen's paper does not extend the classic Gauss-Markov theorem because no nonlinear unbiased estimator exists under his conditions.
Learning from a Biased Sample
Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling.
Adaptive novelty detection with false discovery rate guarantee
This paper studies the semi-supervised novelty detection problem where a set of "typical" measurements is available to the researcher.
Conformal Risk Control
We extend conformal prediction to control the expected value of any monotone loss function.
The Transfer Performance of Economic Models
Economists often estimate models using data from a particular domain, e. g. estimating risk preferences in a particular subject pool or for a specific class of lotteries.
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees.
Design-Robust Two-Way-Fixed-Effects Regression For Panel Data
We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns.
Testing for Outliers with Conformal p-values
We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points; this paves the way to stronger type-I error guarantees.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making.
Conformal Inference of Counterfactuals and Individual Treatment Effects
At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms.
Consistency of Spectral Clustering on Hierarchical Stochastic Block Models
We study the hierarchy of communities in real-world networks under a generic stochastic block model, in which the connection probabilities are structured in a binary tree.
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity is an important yet under-studied property in modern optimization theory.
Variance Reduction with Sparse Gradients
Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients to reduce the variance of stochastic gradients.
On the Adaptivity of Stochastic Gradient-Based Optimization
Stochastic-gradient-based optimization has been a core enabling methodology in applications to large-scale problems in machine learning and related areas.
Hierarchical community detection by recursive partitioning
This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities.
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method
We develop and analyze a procedure for gradient-based optimization that we refer to as stochastically controlled stochastic gradient (SCSG).
