Surreal illustration of scales balanced unevenly with data points, symbolizing AI decision-making and unbalanced data.

Decoding AI's Impact on Treatment Effect Estimates: Can We Trust the Numbers?

Soraya Malik in Tech & Innovation December 2025 • 4 min read.

"A Deep Dive into Calibrated Machine Learning and Unbalanced Datasets"

In an era defined by data, machine learning (ML) is revolutionizing various fields, from healthcare to economics. One critical area where ML shines is in estimating the average treatment effect (ATE). Imagine wanting to know the true impact of a new drug or a job training program. ML methods, especially the double machine learning (DML) estimator, offer powerful tools to uncover these causal relationships.

However, real-world data often throws a wrench in the works. Datasets can be 'unbalanced,' meaning there are significantly more control observations than treated ones. Think of a rare disease study where only a handful of patients receive a novel therapy. This imbalance can lead to unreliable propensity score estimations, undermining the accuracy of ATE estimates. This is where the new research comes in handy.

The study introduces a simple yet effective solution: a calibrated-undersampled DML (CU-DML) estimator. This method cleverly undersamples the data used for propensity score modeling and then calibrates the scores to match the original distribution. The result? A more stable and accurate ATE estimate, even with skewed data. Let’s find out how this works and why it matters for anyone relying on data-driven decisions.

The Problem with Unbalanced Treatment Assignment

Surreal illustration of scales balanced unevenly with data points, symbolizing AI decision-making and unbalanced data.

The double machine learning (DML) estimator, popularized by researchers like Chernozhukov et al. in 2018, has become a go-to method for estimating the average treatment effect (ATE). DML leverages machine learning to handle complex data and provide consistent and asymptotically normal estimates, even when using flexible ML models.

The core challenge arises when datasets exhibit what's known as 'unbalanced treatment assignment.' This means that the number of treated units is significantly smaller than the number of control units. This is common in many fields, such as:

Healthcare: Trials for new drugs often involve a limited number of patients due to cost or ethical considerations.
Economics: Job training programs or policy interventions may only reach a small subset of the population.
Marketing: Targeted advertising campaigns might only be shown to a small group to measure their impact.

When the treatment group is small, machine learning models struggle to accurately estimate the propensity score—the probability of an observation being treated, given its characteristics. Unstable propensity score estimations directly impact the reliability of the ATE estimate. If the propensity score is off, the entire causal inference can be compromised.

The Future of Reliable AI-Driven Insights

The CU-DML estimator offers a practical solution to a common problem in causal inference. By addressing the challenges of unbalanced data, this method enhances the reliability of ATE estimates, making AI-driven insights more trustworthy and actionable. This is especially crucial in fields where decisions have significant real-world consequences, such as healthcare, economics, and policy-making. As machine learning continues to evolve, innovations like CU-DML will play a vital role in ensuring that AI provides accurate and meaningful guidance.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2403.01585,

Title: Calibrating Doubly-Robust Estimators With Unbalanced Treatment Assignment

Subject: econ.em stat.ml

Authors: Daniele Ballinari

Published: 03-03-2024

Everything You Need To Know

What is the average treatment effect (ATE), and why is it important in the context of machine learning?

The average treatment effect (ATE) is a crucial concept in machine learning, representing the average causal impact of a treatment or intervention on a group of individuals. In simpler terms, it helps us understand the effect of something like a new drug or a job training program. Understanding ATE is important because it allows us to make informed decisions in fields like healthcare, economics, and policy-making. When we use machine learning methods like double machine learning (DML) to estimate ATE, we aim to uncover causal relationships, which helps us understand 'what would happen if' scenarios and make better predictions.

What is the double machine learning (DML) estimator, and what are its key advantages and disadvantages?

The double machine learning (DML) estimator is a powerful method for estimating the average treatment effect (ATE) using machine learning. Its primary advantage is its ability to handle complex data and provide consistent and asymptotically normal estimates. DML leverages machine learning models to handle complex data, offering flexibility in model specification and accommodating various data types. A major disadvantage, highlighted in the context, is its sensitivity to unbalanced data. Unbalanced datasets, where there is a significant difference in the number of control versus treated observations, can lead to unstable propensity score estimations and undermine the accuracy of the ATE estimate.

Why is unbalanced data a problem when using DML to estimate the average treatment effect (ATE), and what fields are most affected?

Unbalanced data presents a significant challenge for the double machine learning (DML) estimator because it can lead to inaccurate estimations of the propensity score. When the treatment group is small compared to the control group, machine learning models struggle to accurately estimate the probability of an observation being treated. This directly impacts the reliability of the ATE estimate, potentially leading to incorrect conclusions about the treatment's true impact. This issue is particularly prevalent in healthcare (trials for new drugs), economics (job training programs), and marketing (targeted advertising campaigns), where the treatment group is often a small subset of the population due to various constraints.

How does the calibrated-undersampled DML (CU-DML) estimator work, and what are its benefits?

The calibrated-undersampled DML (CU-DML) estimator addresses the challenges of unbalanced data by implementing a two-step process. First, it undersamples the data used for propensity score modeling, meaning it reduces the size of the larger group (usually the control group) to create a more balanced dataset. Then, it calibrates the propensity scores to match the original distribution. This approach helps stabilize the propensity score estimations, leading to more accurate and reliable ATE estimates, even with skewed data. The primary benefit of CU-DML is that it enhances the trustworthiness and actionability of AI-driven insights, particularly in fields where decisions have significant real-world consequences like healthcare, economics, and policy-making.

How can the CU-DML estimator improve decision-making in fields like healthcare and economics?

The CU-DML estimator can significantly improve decision-making in fields like healthcare and economics by providing more reliable estimates of the average treatment effect (ATE). In healthcare, for instance, if evaluating a new drug, CU-DML can help determine the drug's true impact, even when the trial involves a small number of patients. This leads to better-informed decisions regarding treatment efficacy and patient care. In economics, CU-DML can be used to assess the effectiveness of programs like job training or policy interventions. By accurately estimating ATE, decision-makers can make more informed choices about resource allocation, program design, and policy implementation, ultimately leading to more effective and impactful interventions.