Surreal illustration of causal relationships being investigated through a magnifying glass.

Beyond IVs: Unlock Causal Insights with Marginal Treatment Effects

Soraya Malik in Mind & Education August 2025 • 5 min read.

"A New Approach to Program Evaluation When Traditional Methods Fall Short"

In the world of social sciences, understanding the true impact of interventions and programs is paramount. Whether it's evaluating the effectiveness of an educational initiative or assessing the influence of a new health policy, researchers and policymakers alike need reliable methods for determining cause and effect. This is where causal inference comes into play, a field dedicated to uncovering the underlying mechanisms that drive social phenomena.

One of the most widely used techniques in causal inference is the instrumental variable (IV) approach. IVs are external factors that influence an individual's participation in a program or treatment but do not directly affect the outcome of interest. By leveraging these variables, researchers can isolate the causal effect of the program while accounting for potential confounding factors. However, finding valid IVs can be a daunting task, and in many real-world scenarios, they simply don't exist or are difficult to justify.

But what happens when IVs are elusive? Does this mean that uncovering causal insights is impossible? Fortunately, the answer is no. In recent years, researchers have developed alternative methods for causal inference that do not rely on instrumental variables. One such approach is the marginal treatment effect (MTE), a powerful tool that allows researchers to define, identify, and estimate causal effects even in the absence of traditional IVs.

What is the Marginal Treatment Effect (MTE)?

Surreal illustration of causal relationships being investigated through a magnifying glass.

At its core, the marginal treatment effect (MTE) represents the expected change in outcome for individuals who are induced to participate in a program or treatment due to a small change in their likelihood of participation. Unlike traditional methods that focus on the average effect of a program, MTE allows researchers to examine how the impact varies across different subgroups of the population. This is particularly valuable in understanding heterogeneous treatment effects, where the benefits of a program may be greater for some individuals than others.

The traditional approach to identifying MTE relies heavily on the assumptions of independence, exclusion, and separability (or monotonicity). These assumptions essentially state that the instrumental variable must be independent of the outcome, only affect the outcome through the treatment, and have a consistent effect on participation. However, in many real-world settings, these assumptions are difficult to satisfy, limiting the applicability of the IV-based MTE.

Linear Restriction on Potential Outcome Regression Functions: This condition assumes a linear relationship between the potential outcomes and the observed covariates, simplifying the estimation process.
Nonlinear Restriction on the Propensity Score: This condition requires the propensity score (the probability of participating in the program) to exhibit a nonlinear relationship with the observed covariates. This nonlinearity provides the necessary variation to identify the MTE.
Conditional Mean Independence Restriction: This condition assumes that the unobserved factors affecting the outcome are independent of the observed covariates, conditional on the propensity score. This assumption ensures that the MTE is additively separable into observed and unobserved components.

By relaxing the IV assumptions and imposing these alternative conditions, researchers can unlock a wealth of causal insights that would otherwise remain hidden. The key conditions ensuring the identification of the defined MTE in an environment of essential heterogeneity include:

Implications for Future Research

The development of the MTE approach without relying on IVs represents a significant step forward in the field of causal inference. By providing a framework for defining, identifying, and estimating causal effects under weaker assumptions, this method opens up new avenues for research in a wide range of social science disciplines. From education and healthcare to economics and political science, the MTE has the potential to shed light on the true impact of interventions and policies, leading to more informed decision-making and improved outcomes for individuals and society as a whole.

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.2401.17595,

Title: Marginal Treatment Effects In The Absence Of Instrumental Variables

Subject: econ.em

Authors: Zhewen Pan, Zhengxin Wang, Junsen Zhang, Yahong Zhou

Published: 30-01-2024

Everything You Need To Know

What is the main challenge in causal inference, and how does the Marginal Treatment Effect (MTE) address it?

The primary challenge in causal inference is determining the true impact of programs or interventions, isolating cause and effect from confounding factors. Traditional methods often rely on Instrumental Variables (IVs). However, when finding valid IVs is difficult or impossible, the Marginal Treatment Effect (MTE) provides an alternative. The MTE allows researchers to estimate causal effects even without IVs, offering a powerful way to assess program effectiveness.

How does the MTE differ from the Instrumental Variable (IV) approach, and what are the advantages of using the MTE?

The Instrumental Variable (IV) approach uses external factors (IVs) to isolate causal effects. The MTE, on the other hand, provides an alternative when IVs are not available or difficult to justify. The MTE offers advantages, especially in scenarios with essential heterogeneity. It allows researchers to analyze how program impacts vary across different population subgroups. While the IV approach focuses on the average effect, the MTE enables a more granular understanding of heterogeneous treatment effects.

What are the core assumptions or conditions required for identifying the Marginal Treatment Effect (MTE), and how do they differ from those of the IV approach?

Identifying the MTE involves specific conditions that differ from the IV approach. These include the Linear Restriction on Potential Outcome Regression Functions, the Nonlinear Restriction on the Propensity Score, and the Conditional Mean Independence Restriction. These conditions are imposed to relax the assumptions associated with IVs. They ensure that the MTE can be accurately estimated, even when the traditional IV assumptions of independence, exclusion, and separability are difficult to satisfy.

Can you explain the implications of the Linear Restriction on Potential Outcome Regression Functions, the Nonlinear Restriction on the Propensity Score, and the Conditional Mean Independence Restriction?

The Linear Restriction on Potential Outcome Regression Functions assumes a linear relationship between potential outcomes and observed covariates, simplifying the estimation process. The Nonlinear Restriction on the Propensity Score requires a nonlinear relationship between the propensity score (probability of program participation) and observed covariates, providing necessary variation for MTE identification. The Conditional Mean Independence Restriction assumes that unobserved factors affecting the outcome are independent of the observed covariates, conditioned on the propensity score, ensuring the MTE is additively separable into observed and unobserved components.

In what fields of social science can the Marginal Treatment Effect (MTE) be applied, and how can it influence decision-making?

The Marginal Treatment Effect (MTE) can be applied across various social science fields, including education, healthcare, economics, and political science. By providing a framework for estimating causal effects even without traditional IVs, the MTE can shed light on the true impact of interventions and policies. This leads to more informed decision-making, enabling policymakers and researchers to assess program effectiveness with greater accuracy and ultimately improve outcomes for individuals and society.