Distorted event study graph symbolizing potential misinterpretations in economic data analysis.

Decoding Event Studies: How New Economic Methods Can Mislead

Avery Sinclair in Business & Economy August 2025 • 4 min read.

"A straightforward guide to interpreting event-study plots, highlighting the potential pitfalls of recent difference-in-differences methods and offering practical solutions for accurate analysis."

Modern economic research relies heavily on difference-in-differences (DiD) analyses, often visualized through event-study plots. These plots are essential for evaluating how trends between treated and comparison groups diverge before and after an intervention. However, recent advancements in DiD methods, while powerful, can sometimes produce misleading event-study plots, especially for younger audiences or those new to the field.

Many researchers may find that the default plots generated by software for these newer methods don't always align with traditional two-way fixed effects (TWFE) event studies. This discrepancy can lead to misinterpretations, such as perceiving a sudden change at the intervention point when none exists according to traditional measures. This article addresses these challenges, focusing on methods like those developed by de Chaisemartin and D'Haultfœuille (2020), Callaway and Sant'Anna (2021), and Borusyak, Jaravel and Spiess (2024).

This guide clarifies why these differences arise and offers practical recommendations for constructing and interpreting event-study plots more accurately. We aim to equip you with the knowledge to confidently navigate these advanced methods and avoid common pitfalls in your economic analysis.

The Event-Study Plot Paradox: When New Methods Show False Signals

Distorted event study graph symbolizing potential misinterpretations in economic data analysis.

Event-study plots derived from traditional TWFE specifications typically rely on visual inference. Researchers look for deviations in trends between the treated and comparison groups around the time of treatment. A convincing plot often shows a clear discontinuity or change in slope, indicating a real effect. However, newer DiD methods can generate plots that display a kink or jump at the treatment time, even when the underlying TWFE analysis shows a straight line, suggesting no actual change.

This paradox stems from how these new methods construct pre-treatment coefficients differently from post-treatment coefficients. In essence, they introduce an asymmetry that can distort the visual representation of the data. This means that the familiar heuristics used for interpreting TWFE plots can be misleading when applied to plots generated by these newer methods.

Asymmetric Coefficient Construction: Newer methods often calculate pre- and post-treatment coefficients using different approaches, leading to potential distortions.
Visual Misinterpretations: Kinks or jumps in event-study plots might not always indicate a real treatment effect but could be artifacts of the method.
Heuristic Pitfalls: Relying solely on visual cues from traditional TWFE plots can lead to incorrect conclusions when using new DiD methods.

To illustrate this, consider a scenario where a new policy is implemented. Using traditional TWFE, the event-study plot shows a consistent, linear trend before and after the policy change, indicating no significant impact. However, applying a newer DiD method might produce a plot with a noticeable kink at the policy implementation date, suggesting a change that isn't actually there. This discrepancy highlights the need for caution and a deeper understanding of the methods being used.

Best Practices for Accurate Event-Study Interpretation

To avoid misinterpretations and ensure the robustness of your findings, it's crucial to adopt a more nuanced approach when working with event-study plots from newer DiD methods. Always consider the specific construction of the coefficients, and don't rely solely on visual cues. By understanding these potential pitfalls, you can ensure that your economic analysis is both accurate and insightful.

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.2401.12309,

Title: Interpreting Event-Studies From Recent Difference-In-Differences Methods

Subject: econ.em stat.me

Authors: Jonathan Roth

Published: 22-01-2024

Everything You Need To Know

What is the key challenge when using newer Difference-in-Differences (DiD) methods in economic event studies?

The primary challenge lies in the potential for newer DiD methods to generate misleading event-study plots. Unlike traditional Two-Way Fixed Effects (TWFE) event studies, these newer methods can sometimes produce plots that display a kink or jump at the treatment time, even when no actual change exists according to traditional measures. This discrepancy can lead to misinterpretations of treatment effects. This arises because of asymmetric coefficient construction, where pre- and post-treatment coefficients are calculated using different approaches, leading to potential distortions. Relying solely on visual cues from traditional TWFE plots can, therefore, lead to incorrect conclusions when using new DiD methods, such as those developed by de Chaisemartin and D'Haultfœuille (2020), Callaway and Sant'Anna (2021), and Borusyak, Jaravel and Spiess (2024).

Why might event-study plots from newer DiD methods show a 'false signal' of treatment effects?

Event-study plots from newer DiD methods can show 'false signals' due to how these methods construct pre-treatment and post-treatment coefficients differently. This asymmetry can distort the visual representation of the data, leading to kinks or jumps at the treatment time that do not reflect a real treatment effect. Traditional TWFE specifications typically rely on visual inference, looking for deviations in trends between the treated and comparison groups around the time of treatment. However, newer DiD methods introduce an asymmetry that can distort the visual representation of the data.

How do traditional Two-Way Fixed Effects (TWFE) event studies differ from those produced by newer Difference-in-Differences (DiD) methods, and what implications does this have for interpretation?

Traditional TWFE event studies often rely on visual inference to identify treatment effects, where researchers look for deviations in trends between treated and comparison groups around the time of treatment. Newer DiD methods, such as those by de Chaisemartin and D'Haultfœuille (2020), Callaway and Sant'Anna (2021), and Borusyak, Jaravel and Spiess (2024), can generate plots that display a kink or jump at the treatment time, even when the underlying TWFE analysis shows a straight line, suggesting no actual change. This discrepancy arises from asymmetric coefficient construction in the newer methods. This means that the familiar heuristics used for interpreting TWFE plots can be misleading when applied to plots generated by these newer methods, necessitating a more nuanced approach.

What are some best practices for accurately interpreting event-study plots generated from newer Difference-in-Differences (DiD) methods?

To accurately interpret event-study plots from newer DiD methods, it's crucial to avoid relying solely on visual cues and to consider the specific construction of the coefficients. A more nuanced approach is needed, acknowledging that kinks or jumps might not always indicate a real treatment effect but could be artifacts of the method. Understanding these potential pitfalls helps ensure that your economic analysis is both accurate and insightful. Methods like those developed by de Chaisemartin and D'Haultfœuille (2020), Callaway and Sant'Anna (2021), and Borusyak, Jaravel and Spiess (2024) require careful attention to the construction of pre- and post-treatment coefficients.

In the context of event studies, what does 'asymmetric coefficient construction' mean, and why is it important to understand?

'Asymmetric coefficient construction' refers to the practice in newer Difference-in-Differences (DiD) methods where pre- and post-treatment coefficients are calculated using different approaches. This is important because it can lead to distortions in event-study plots, causing them to display a kink or jump at the treatment time, even when no actual change exists according to traditional Two-Way Fixed Effects (TWFE) analysis. Understanding this asymmetry is crucial for avoiding misinterpretations of treatment effects and ensuring the accuracy of economic analysis. Neglecting this can lead to false conclusions about the impact of interventions when using methods like those developed by de Chaisemartin and D'Haultfœuille (2020), Callaway and Sant'Anna (2021), and Borusyak, Jaravel and Spiess (2024).