21 research outputs found
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
There is fast-growing literature on estimating heterogeneous treatment
effects via random forests in observational studies. However, there are few
approaches available for right-censored survival data. In clinical trials,
right-censored survival data are frequently encountered. Quantifying the causal
relationship between a treatment and the survival outcome is of great interest.
Random forests provide a robust, nonparametric approach to statistical
estimation. In addition, recent developments allow forest-based methods to
quantify the uncertainty of the estimated heterogeneous treatment effects. We
propose causal survival forests that directly target on estimating the
treatment effect from an observational study. We establish consistency and
asymptotic normality of the proposed estimators and provide an estimator of the
asymptotic variance that enables valid confidence intervals of the estimated
treatment effect. The performance of our approach is demonstrated via extensive
simulations and data from an HIV study
Federated Learning of Causal Effects from Incomplete Observational Data
Decentralized and incomplete data sources are prevalent in real-world
applications, posing a formidable challenge for causal inference. These sources
cannot be consolidated into a single entity owing to privacy constraints, and
the presence of missing values within them can potentially introduce bias to
the causal estimands. We introduce a new approach for federated causal
inference from incomplete data, enabling the estimation of causal effects from
multiple decentralized and incomplete data sources. Our approach disentangles
the loss function into multiple components, each corresponding to a specific
data source with missing values. Our approach accounts for the missing data
under the missing at random assumption, while also estimating higher-order
statistics of the causal estimands. Our method recovers the conditional
distribution of missing confounders given the observed confounders from the
decentralized data sources to identify causal effects. Our framework estimates
heterogeneous causal effects without the sharing of raw training data among
sources, which helps to mitigate privacy risks. The efficacy of our approach is
demonstrated through a collection of simulated and real-world instances,
illustrating its potential and practicality.Comment: Preprin
Higher-Order Orthogonal Causal Learning for Treatment Effect
Most existing studies on the double/debiased machine learning method
concentrate on the causal parameter estimation recovering from the first-order
orthogonal score function. In this paper, we will construct the
-order orthogonal score function for estimating the average
treatment effect (ATE) and present an algorithm that enables us to obtain the
debiased estimator recovered from the score function. Such a higher-order
orthogonal estimator is more robust to the misspecification of the propensity
score than the first-order one does. Besides, it has the merit of being
applicable with many machine learning methodologies such as Lasso, Random
Forests, Neural Nets, etc. We also undergo comprehensive experiments to test
the power of the estimator we construct from the score function using both the
simulated datasets and the real datasets
The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies
A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal forest and generic machine learning methods, in the context of both average and heterogeneous treatment effects. We illustrate the implementation of these methods in a variety of settings and highlight the relevance and value added relative to traditional methods used in the original studies
Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items
Problem definition: Mining for heterogeneous responses to an intervention is
a crucial step for data-driven operations, for instance to personalize
treatment or pricing. We investigate how to estimate price sensitivity from
transaction-level data. In causal inference terms, we estimate heterogeneous
treatment effects when (a) the response to treatment (here, whether a customer
buys a product) is binary, and (b) treatment assignments are partially observed
(here, full information is only available for purchased items).
Methodology/Results: We propose a recursive partitioning procedure to estimate
heterogeneous odds ratio, a widely used measure of treatment effect in medicine
and social sciences. We integrate an adversarial imputation step to allow for
robust inference even in presence of partially observed treatment assignments.
We validate our methodology on synthetic data and apply it to three case
studies from political science, medicine, and revenue management. Managerial
Implications: Our robust heterogeneous odds ratio estimation method is a simple
and intuitive tool to quantify heterogeneity in patients or customers and
personalize interventions, while lifting a central limitation in many revenue
management data