Causal inference in health policy using interconnected data nodes.

Decoding Difference-in-Differences: A Modern Guide to Health Policy Analysis

Reese Marlowe in Health & Wellness November 2025 • 4 min read.

"Navigate the complexities of DiD with our comprehensive review, designed for medical and health policy researchers."

In the world of health policy, understanding the real-world impact of new policies and programs is paramount. One of the most popular and powerful tools for this task is the Difference-in-Differences (DiD) method. DiD is an observational causal inference technique used to estimate the treatment effects of interventions, by comparing the changes in outcomes over time between a treatment group and a control group.

At its core, DiD relies on a critical assumption known as the 'parallel trends assumption'. This assumes that, in the absence of the intervention, the treatment and comparison groups would have followed similar trajectories. While traditionally considered straightforward, recent years have witnessed significant advancements in DiD methodologies, making it crucial for researchers to stay updated.

This article provides a comprehensive review of modern DiD methods, tailored for medical and health policy researchers. We synthesize recent innovations, address common pitfalls, and offer practical guidance for conducting robust analyses. By understanding these modern approaches, researchers can more effectively evaluate policy impacts and inform evidence-based decision-making.

Understanding the Core of Difference-in-Differences

Causal inference in health policy using interconnected data nodes.

The foundation of DiD lies in its central 'parallel trends assumption'. This assumption posits that, on average, the treatment and comparison groups would have maintained similar outcome trajectories had the intervention not occurred. Furthermore, DiD assumes no anticipation effects (i.e., the treatment does not affect the treatment group before implementation) and no spillover effects (i.e., the treatment does not affect the comparison group).

When these assumptions hold, DiD allows us to estimate what would have happened in the treatment group without the intervention. This counterfactual scenario, represented by dotted lines in Figure 1 of the original text, helps us isolate the average treatment effect on the treated (ATT).

Stable Pre-Trend: Outcome trajectory is consistent before the intervention, with or without a fixed level difference between groups.
Changing Pre-Trend: Different outcome trajectories prior to the intervention, requiring careful consideration.
Treatment Effects: Understanding whether the treatment causes immediate level changes, gradual trajectory shifts, or delayed impacts.

The most common way to implement DiD is through a two-way fixed effects (TWFE) model, estimated using ordinary least squares (OLS) regression. The equation below mathematically expresses a static TWFE model, where the treatment effect is isolated. The term “DiD” and “TWFE” are often used interchangeably; TWFE is a simple estimation approach used in a subset of DiD analyses.

Strengthening Policy Insights with Modern DiD

By carefully considering potential confounders, employing appropriate estimation techniques, and conducting thorough sensitivity analyses, researchers can strengthen the validity and reliability of their DiD findings. These recent innovations empower researchers to generate more robust evidence and inform effective policy recommendations.

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.2408.04617,

Title: Difference-In-Differences For Health Policy And Practice: A Review Of Modern Methods

Subject: stat.ap econ.em

Authors: Shuo Feng, Ishani Ganguli, Youjin Lee, John Poe, Andrew Ryan, Alyssa Bilinski

Published: 08-08-2024

Everything You Need To Know

What is Difference-in-Differences (DiD) and how is it used in health policy analysis?

Difference-in-Differences (DiD) is an observational causal inference technique used to evaluate the impact of interventions, especially in health policy. It works by comparing the changes in outcomes over time between a treatment group (affected by a new policy or program) and a control group (not affected). This allows researchers to estimate the treatment effects of interventions. The goal is to understand the real-world impact of new policies and programs to inform evidence-based decision-making.

What is the 'parallel trends assumption' in Difference-in-Differences (DiD), and why is it important?

The 'parallel trends assumption' is a core element of Difference-in-Differences (DiD). This assumption states that, in the absence of the intervention, the treatment and comparison groups would have followed similar outcome trajectories over time. If this assumption holds, DiD can isolate the average treatment effect on the treated (ATT). Violations of this assumption, such as different pre-intervention trajectories, can lead to biased estimates of the intervention's effect, making the analysis unreliable.

What are the potential pitfalls when using Difference-in-Differences (DiD) and how can researchers address them?

Several pitfalls can affect Difference-in-Differences (DiD) analysis. One is the violation of the 'parallel trends assumption', which could be caused by pre-existing differences or other confounding factors. Anticipation effects, where the treatment group is affected before the intervention, and spillover effects, where the comparison group is affected, can also bias the results. Researchers can mitigate these pitfalls by carefully considering potential confounders, using appropriate estimation techniques, and conducting thorough sensitivity analyses. For example, employing a Two-Way Fixed Effects (TWFE) model, which is often used in DiD, can help control for time-invariant and group-invariant factors.

What are the three types of pre-trends, and how should researchers approach each one?

Researchers must understand the implications of Stable, Changing, and Treatment Effects pre-trends to correctly apply Difference-in-Differences (DiD) analysis. A 'Stable Pre-Trend' suggests outcome trajectories are consistent before the intervention, whether or not there's a difference between the groups. With a 'Changing Pre-Trend', different outcome trajectories prior to the intervention require careful consideration and adjustment. 'Treatment Effects' considerations involve understanding how the treatment influences immediate changes, gradual shifts, or delayed effects. Each pre-trend type demands specific analytical approaches to ensure accurate effect estimation.

How is the Two-Way Fixed Effects (TWFE) model related to Difference-in-Differences (DiD), and what does it help researchers achieve?

The Two-Way Fixed Effects (TWFE) model is a common method to implement Difference-in-Differences (DiD) and estimate the treatment effect. Often, the terms 'DiD' and 'TWFE' are used interchangeably, although TWFE is a specific estimation approach within DiD. TWFE uses ordinary least squares (OLS) regression to isolate the treatment effect. By using TWFE, researchers can control for both time-invariant and group-invariant factors, which strengthens the reliability of the analysis and ensures more robust evidence for policy recommendations.