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Found 46 papers, 19 papers with code
Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments
This representation allows us to introduce an identifying assumption, so-called copula invariance, that restricts the local dependence of the copula with respect to the treatment propensity.
Applied Causal Inference Powered by ML and AI
An introduction to the emerging fusion of machine learning and causal inference.
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
First, we assess the importance of data splitting schemes for tuning ML learners within Double Machine Learning.
Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models
However, the estimator is severely biased when the data's time series dimension $T$ is long due to the large degree of overidentification.
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation.
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function.
An MCMC Approach to Classical Estimation
This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods.
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals.
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Therefore, simple plausibility judgments on the maximum explanatory power of omitted variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias.
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
We also propose a Random Forest method which learns a locally linear representation of the Riesz function.
Causal Bias Quantification for Continuous Treatments
We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal models.
A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees
Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i. e. scalar summaries, of machine learning algorithms.
Uniform Inference on High-dimensional Spatial Panel Networks
We propose employing a debiased-regularized, high-dimensional generalized method of moments (GMM) framework to perform inference on large-scale spatial panel networks.
Deeply-Debiased Off-Policy Interval Estimation
Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy.
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models.
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
This paper serves as an introduction to the double machine learning framework and the R package DoubleML.
Vector quantile regression and optimal transport, from theory to numerics
In this paper, we first revisit the Koenker and Bassett variational approach to (univariate) quantile regression, emphasizing its link with latent factor representations and correlation maximization problems.
The Association of Opening K-12 Schools with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis
This paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States.
Adversarial Estimation of Riesz Representers
Furthermore, we use critical radius theory -- in place of Donsker theory -- to prove asymptotic normality without sample splitting, uncovering a ``complexity-rate robustness'' condition.
Nearly optimal central limit theorem and bootstrap approximations in high dimensions
In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of $n$ independent high-dimensional centered random vectors $X_1,\dots, X_n$ over the class of rectangles in the case when the covariance matrix of the scaled average is non-degenerate.
Probability Statistics Theory Statistics Theory 60F05, 62E17
Minimax Semiparametric Learning With Approximate Sparsity
This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression.
Distributional conformal prediction
We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression.
Inference on weighted average value function in high-dimensional state space
Second, we give a correction term for the transition density of the state variable.
Semi-Parametric Efficient Policy Learning with Continuous Actions
Our results also apply if the model does not satisfy our semi-parametric form, but rather we measure regret in terms of the best projection of the true value function to this functional space.
A $t$-test for synthetic controls
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls.
Closing the U.S. gender wage gap requires understanding its heterogeneity
In 2016, the majority of full-time employed women in the U. S. earned significantly less than comparable men.
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
We develop a distribution regression model under endogenous sample selection.
Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important.
Shape-Enforcing Operators for Point and Interval Estimators
A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions.
Uniform Inference in High-Dimensional Gaussian Graphical Models
Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures.
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers
To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter.
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
We extend conformal inference to general settings that allow for time series data.
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence
The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross-fitted residuals.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments.
Identification of hedonic equilibrium and nonseparable simultaneous equations
This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity.
Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions
This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools.
Double/Debiased/Neyman Machine Learning of Treatment Effects
A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).
Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes
In both applications, the outcomes of interest are discrete rendering existing inference methods invalid for obtaining uniform confidence bands for quantile and quantile effects functions.
Methodology Econometrics 62F25, 62G15, 62P20
hdm: High-Dimensional Metrics
In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced.
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
High-Dimensional Metrics in R
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models.
The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages
They are as convenient and easy to report in practice as the conventional average partial effects.
Methodology Econometrics
Vector Quantile Regression: An Optimal Transport Approach
Under correct specification, the notion produces strong representation, $Y=\beta \left(U\right) ^\top f(Z)$, for $f(Z)$ denoting a known set of transformations of $Z$, where $u \longmapsto \beta(u)^\top f(Z)$ is a monotone map, the gradient of a convex function, and the quantile regression coefficients $u \longmapsto \beta(u)$ have the interpretations analogous to that of the standard scalar quantile regression.
Methodology 49Q20, 49Q10, 90B20
Program Evaluation and Causal Inference with High-Dimensional Data
In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE).
