Neural network over a world map representing causal inference algorithms.

Unlock Causal Inference with Forecasting Algorithms: A Guide to SyNBEATS and Panel Data Analysis

Lena Kashyap in Tech & Innovation December 2025 • 4 min read.

"Discover how forecasting algorithms, particularly SyNBEATS, are revolutionizing causal inference in social science by improving the accuracy and reliability of panel data analysis."

Causal inference with panel data stands as a critical yet intricate endeavor in social science research. Imagine a scenario where a state introduces a new policy. The core question is: What impact does this policy have on specific outcomes? This is where the challenge begins, often framed as a prediction problem. To accurately assess the policy's impact, researchers must estimate the counterfactual—what would have happened had the policy not been implemented.

Traditional methods often fall short in providing precise counterfactual estimations. However, recent advancements in forecasting algorithms are paving the way for more accurate causal inference. One such advancement is the adaptation of deep neural architectures, like the N-BEATS algorithm, to better impute the counterfactual evolution of treated units. This evolution has led to the development of innovative tools such as SyNBEATS, which significantly outperforms conventional methods.

In this article, we delve into the mechanics of SyNBEATS, exploring how it harnesses advances in forecasting literature to improve causal inference in panel data settings. We will explore its architecture, compare its performance against existing methodologies, and discuss its potential impact on future research and policy-making.

What is SyNBEATS and How Does It Enhance Causal Inference?

Neural network over a world map representing causal inference algorithms.

SyNBEATS, short for Synthetic N-BEATS, leverages a deep neural architecture originally designed for time series forecasting. The primary goal is to accurately impute the counterfactual evolution of a treated unit, essentially predicting what would have occurred had a specific intervention not taken place. By adapting the N-BEATS algorithm, SyNBEATS significantly improves the accuracy of these predictions, offering a robust alternative to traditional methods.

The core innovation lies in its ability to incorporate data from control units to enhance the forecasting model for the treated unit. Unlike single-unit time series models, SyNBEATS considers the time series of control unit outcomes as additional features, effectively using contemporaneous outcomes of control states as “leading indicators” for the potential untreated outcome of the treated state. This approach is particularly valuable when analyzing panel data, where interdependencies between different units can significantly impact outcomes.

Superior Performance: SyNBEATS consistently outperforms commonly employed methods such as synthetic controls and two-way fixed effects.
Comparable Accuracy: It achieves comparable or more accurate performance relative to recently proposed methods like synthetic difference-in-differences and matrix completion.
Publicly Available: An implementation of the SyNBEATS estimator is available for public use, making it accessible to researchers and practitioners.

The success of SyNBEATS hinges on several key components, including its model architecture and efficient use of time-series data. By breaking down the signal from input data step-by-step, SyNBEATS can capture complex relationships that traditional models might miss. This approach ensures that the model fully utilizes the available data, converging quickly even in settings with limited data availability.

The Future of Causal Inference with Advanced Algorithms

The introduction of SyNBEATS marks a significant step forward in the field of causal inference. Its ability to leverage both horizontal and vertical data, combined with its sophisticated model architecture, provides a powerful tool for researchers and policymakers. As the algorithm continues to be refined and applied across various domains, it promises to unlock new insights and improve the accuracy of data-driven decision-making. The journey towards more reliable and nuanced causal inference is just beginning, and SyNBEATS is at the forefront of this exciting evolution.

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.2208.03489,

Title: Forecasting Algorithms For Causal Inference With Panel Data

Subject: econ.em stat.ml

Authors: Jacob Goldin, Julian Nyarko, Justin Young

Published: 06-08-2022

Everything You Need To Know

What is SyNBEATS and how does it improve causal inference in the context of panel data?

SyNBEATS, which stands for Synthetic N-BEATS, is a forecasting algorithm designed to enhance causal inference. It leverages a deep neural architecture to impute the counterfactual outcomes in panel data, meaning it predicts what would have happened if an intervention had not occurred. It utilizes the N-BEATS algorithm, adapting it to incorporate data from control units. This is achieved by using the time series of control unit outcomes as additional features, which serves as leading indicators for the potential untreated outcomes of the treated units. This approach leads to more accurate predictions and enhances data-driven decision-making in social science research, outperforming traditional methods like synthetic controls and two-way fixed effects.

How does SyNBEATS utilize control units to enhance causal inference, and why is this approach beneficial?

SyNBEATS uses control units by considering their time series outcomes as additional features within the forecasting model for the treated unit. This means that the algorithm uses contemporaneous outcomes of control states as leading indicators for the potential untreated outcome of the treated state. This method is particularly valuable because it allows the algorithm to understand the interdependencies between different units in panel data, which can significantly impact outcomes. By incorporating data from control units, SyNBEATS offers a more nuanced and accurate estimation of the counterfactual, leading to improved causal inference compared to methods that don't account for such relationships.

What are the key advantages of using SyNBEATS over traditional methods like synthetic controls and two-way fixed effects?

SyNBEATS offers several key advantages over traditional methods. Primarily, it consistently outperforms methods like synthetic controls and two-way fixed effects in causal inference tasks. It achieves comparable or more accurate performance than more recently proposed methods, such as synthetic difference-in-differences and matrix completion. The architecture allows for capturing complex relationships that traditional models might miss, ensuring that the model fully utilizes available data. SyNBEATS's publicly available implementation makes it accessible for researchers and practitioners, promoting wider use and further development in the field of causal inference.

How does the architecture of SyNBEATS enable superior performance in causal inference?

The architecture of SyNBEATS is designed to break down the signal from input data step-by-step, enabling it to capture complex relationships that traditional models might miss. This approach ensures that the model fully utilizes the available data. It efficiently uses time-series data, converging quickly even when the data available is limited. By adapting the N-BEATS algorithm, SyNBEATS significantly improves the accuracy of these predictions by incorporating data from control units, thus providing a robust alternative to traditional methods.

What is the potential impact of SyNBEATS on future research and policy-making in social science?

SyNBEATS has the potential to significantly impact future research and policy-making. By providing a more accurate and reliable method for causal inference, SyNBEATS enables researchers and policymakers to make more informed, data-driven decisions. Its ability to leverage both horizontal and vertical data, combined with its sophisticated model architecture, unlocks new insights in various domains. As the algorithm continues to be refined and applied across different areas, it promises to improve the accuracy of research findings and the effectiveness of policy interventions, marking a substantial advancement in the field of social science.