Interconnected nodes representing causality emerging from data.

Unraveling Causality: Can We Really Determine Cause and Effect Without Exogenous Factors?

Nico Varela in Tech & Innovation December 2025 • 3 min read.

"New research explores how to identify causal relationships using observational data and sophisticated statistical methods, even when traditional methods fall short."

Determining cause and effect is crucial in fields from economics to healthcare. Traditionally, researchers rely on exogenous variation—external factors that influence one variable but not the other directly—to identify causal relationships. But what happens when these factors are absent? Can we still confidently say that A causes B, or are we left guessing?

A new study tackles this challenge head-on, proposing innovative methods to establish causality even without exogenous variables. By using relatively weak model restrictions and exploiting the inherent dependence between a residual and the potential cause, researchers are finding new ways to unlock causal insights from observational data.

This approach isn't just theoretical. It has practical implications for understanding complex systems where direct manipulation is impossible or unethical. From analyzing economic trends to understanding health outcomes, the ability to discern causality from observation alone opens exciting new possibilities.

The Challenge of Reverse Causality and Endogeneity

Interconnected nodes representing causality emerging from data.

Endogeneity is a central problem in econometrics. It occurs when the independent variable is correlated with the error term, leading to biased estimates of causal effects. Reverse causality, where the dependent variable influences the independent variable, is a prime example of endogeneity.

Consider the relationship between income and education. Higher education often leads to higher income, but it's also plausible that individuals from wealthier backgrounds have greater access to educational opportunities. Disentangling these effects requires careful analysis and methods to address endogeneity.

Traditional Solutions: Instrumental variables are often used to address endogeneity. These are variables that influence the independent variable but do not directly affect the dependent variable.
Limitations: Finding valid instrumental variables can be difficult. They must be both relevant (strongly correlated with the independent variable) and exogenous (uncorrelated with the error term).
A New Approach: Recent research explores methods to identify causality without relying on exogenous variation. This involves using relatively weak model restrictions, and using complex models that show how the true functional relationship is nonlinear and that error terms are additively separable.

The key idea is that the dependence between the error term and the purported cause provides valuable information about the causal direction. By carefully modeling this dependence, researchers can distinguish between true causal relationships and spurious correlations.

Implications and Future Directions

The ability to determine causal relationships from observational data has profound implications for various fields. In economics, it can help us understand the effects of policy interventions. In healthcare, it can shed light on the causes of disease. In social science, it can inform our understanding of social phenomena.

About this Article -

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2107.05936,

Title: Testability Of Reverse Causality Without Exogenous Variation

Subject: econ.em

Authors: Christoph Breunig, Patrick Burauel

Published: 13-07-2021

Everything You Need To Know

What is the primary challenge in determining cause and effect, and how does the new research address it?

The primary challenge is the absence of exogenous variation, or external factors that researchers traditionally use to identify causal relationships. The new research tackles this by proposing innovative methods to establish causality even without exogenous variables. This involves using relatively weak model restrictions and exploiting the inherent dependence between a residual and the potential cause to unlock causal insights from observational data.

Why is determining cause and effect so crucial in fields like economics and healthcare?

Determining cause and effect is crucial because it allows us to understand how different factors influence outcomes and make informed decisions. In economics, it can help us understand the effects of policy interventions. In healthcare, it can shed light on the causes of disease. By understanding causal relationships, we can design more effective policies, treatments, and interventions to improve outcomes in these critical areas.

What is 'endogeneity,' and why does it complicate the analysis of cause and effect, and how can this be addressed?

Endogeneity occurs when the independent variable is correlated with the error term, leading to biased estimates of causal effects. Reverse causality, where the dependent variable influences the independent variable, is a prime example of endogeneity. Addressing endogeneity requires careful analysis and methods. Traditional solutions include using instrumental variables, which are variables that influence the independent variable but do not directly affect the dependent variable. Recent research explores methods to identify causality without relying on exogenous variation, using weak model restrictions, and complex models that exploit the dependence between the error term and the purported cause.

How do instrumental variables work in addressing endogeneity, and what are the limitations of using them?

Instrumental variables are used to address endogeneity by influencing the independent variable but not directly affecting the dependent variable. The limitations of using instrumental variables are that finding valid instrumental variables can be difficult. They must be both relevant (strongly correlated with the independent variable) and exogenous (uncorrelated with the error term). If an instrumental variable fails to meet these criteria, it can lead to misleading results and incorrect conclusions about causality.

How does the new approach to determining causality differ from traditional methods, and what are the potential implications of this new approach?

The new approach differs from traditional methods by establishing causality without relying on exogenous variation. Instead of using instrumental variables, it involves using relatively weak model restrictions and exploiting the inherent dependence between a residual and the potential cause. The potential implications of this new approach are profound. It can help us understand the effects of policy interventions in economics, shed light on the causes of disease in healthcare, and inform our understanding of social phenomena in social science. This approach opens new possibilities in fields where direct manipulation is impossible or unethical, allowing us to discern causality from observation alone.