Data streams forming a brain, symbolizing machine learning for policy evaluation

Beyond Control Groups: How Machine Learning Is Revolutionizing Policy Evaluation

Theo Raines in Tech & Innovation December 2025 • 4 min read.

"Discover a cutting-edge approach to causal inference that overcomes the limitations of traditional methods, enabling robust policy analysis even without a control group."

For decades, economists and policy analysts have relied on a core set of tools to understand the impact of various interventions. Methods such as difference-in-differences, synthetic control, and fixed effects models have become staples in the field. However, these approaches share a common Achilles' heel: they require a credible control group—a set of untreated units that closely mirror the treated group, allowing for a comparison of outcomes. But what happens when a control group simply doesn't exist?

In many real-world scenarios, finding a suitable control group is a significant challenge. Sometimes, the intervention affects everyone simultaneously, such as a nationwide policy change or a global crisis. Other times, even when only a subset of units is treated, spillover effects or other forms of interference can contaminate the untreated units, making them unsuitable as a control. In these situations, traditional methods break down, leaving policymakers in the dark about the true impact of their decisions.

Enter the Machine Learning Control Method (MLCM), a novel approach that's changing the game for causal inference. By leveraging the power of machine learning, the MLCM can estimate causal effects without relying on a traditional control group. This opens up a whole new world of possibilities for evaluating policies and understanding complex phenomena, offering insights that were previously out of reach.

The Machine Learning Control Method (MLCM): A New Paradigm

Data streams forming a brain, symbolizing machine learning for policy evaluation

The MLCM represents a significant departure from traditional causal inference techniques. Instead of comparing treated units to a control group, it focuses on building a predictive model of what would have happened to the treated units in the absence of the intervention. This is achieved through a process called counterfactual forecasting, where machine learning algorithms are trained on pre-intervention data to predict post-intervention outcomes.

At its core, the MLCM is a versatile technique that can leverage any available supervised machine learning algorithm. It identifies and estimates various policy-relevant causal parameters, including individual, average, and conditional average treatment effects (CATEs), making it applicable in numerous real-world settings, even without a control group. This method is flexible with data structure, meaning it can be used across many panel settings, including contexts with staggered adoption and short panels.

Versatility: Works with any supervised ML algorithm.
Flexibility: Suitable for various panel settings, including short panels and staggered adoption.
Comprehensive: Estimates individual, average, and conditional average treatment effects.

The intuition behind the MLCM is straightforward: when you can't rely on untreated units to build a counterfactual, you forecast it. By training supervised ML techniques on pre-treatment data, you can predict the evolution of post-treatment outcomes in the absence of the intervention. The treatment effects are then estimated as the difference between the observed and forecasted post-treatment outcomes.

Unlocking New Possibilities for Policy Evaluation

The MLCM offers a powerful new tool for policymakers and researchers, enabling them to evaluate the impact of interventions in situations where traditional methods fall short. By moving beyond the reliance on control groups, this approach opens up new avenues for understanding complex phenomena and informing evidence-based decision-making. As machine learning continues to evolve, the MLCM promises to become an increasingly valuable asset in the quest to understand and improve the world around us.

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.2312.05858,

Title: Causal Inference And Policy Evaluation Without A Control Group

Subject: econ.em stat.ap

Authors: Augusto Cerqua, Marco Letta, Fiammetta Menchetti

Published: 10-12-2023

Everything You Need To Know

What are the conventional methods used by economists and policy analysts to assess the impact of interventions, and what is their primary limitation?

Economists and policy analysts have traditionally used methods like difference-in-differences, synthetic control, and fixed effects models. However, these methods require a credible control group to compare outcomes, which is a significant limitation when a suitable control group is unavailable due to universal interventions or spillover effects.

How does the Machine Learning Control Method (MLCM) differ from traditional causal inference techniques like difference-in-differences or synthetic control?

The Machine Learning Control Method (MLCM) departs from traditional methods by not relying on a control group. Instead, it builds a predictive model using machine learning algorithms trained on pre-intervention data to forecast what would have happened to the treated units without the intervention. It estimates causal effects by comparing observed post-treatment outcomes with these forecasts, offering a solution when a traditional control group is not available or reliable. The traditional methods compare against a control group and MLCM forecasts without a control group.

How can the Machine Learning Control Method (MLCM) be applied in situations where traditional methods like difference-in-differences are not suitable?

The Machine Learning Control Method (MLCM) is particularly useful in scenarios where finding a suitable control group is challenging, such as when an intervention affects everyone simultaneously or when spillover effects contaminate potential control units. In these situations, traditional methods like difference-in-differences break down. However, the MLCM can still estimate causal effects by building a predictive model of what would have happened without the intervention, based on pre-intervention data.

What types of causal parameters can the Machine Learning Control Method (MLCM) estimate, and how does this contribute to its versatility in policy evaluation?

The Machine Learning Control Method (MLCM) can estimate individual, average, and conditional average treatment effects (CATEs). This comprehensiveness makes it applicable in various real-world settings, even without a control group. Its data structure flexibility means it can be used across many panel settings, including those with staggered adoption and short panels, enhancing its versatility.

What is the core intuition behind the Machine Learning Control Method (MLCM), and why is this approach valuable for policymakers?

The core intuition behind the Machine Learning Control Method (MLCM) is to forecast outcomes when a reliable control group is unavailable. By training supervised machine learning techniques on pre-treatment data, the MLCM predicts post-treatment outcomes in the absence of intervention. This method is valuable for policymakers because it enables them to evaluate the impact of interventions in situations where traditional methods are inadequate, thus informing evidence-based decision-making in complex scenarios.