Surreal illustration of a mind with interconnected gears representing causal relationships.

Decoding Decisions: How Causal Judgments Shape What We Choose

Jordan Keane in Mind & Education August 2025 • 4 min read.

"Uncover the hidden 'causal models' that drive our choices, and how understanding them can lead to better decisions."

Every day, we make countless decisions, from the mundane to the life-altering. While we often think we're acting rationally, a hidden force shapes our choices: our understanding of cause and effect. Imagine the Federal Reserve deciding whether to adjust interest rates. They don't just consider the immediate impact; they also weigh the potential ripple effects, like changes in unemployment, and how these effects might be influenced by other economic factors.

This intricate web of cause-and-effect relationships forms our 'causal judgments,' and they're surprisingly personal. What one person believes to be a direct consequence, another might see as a distant possibility. This divergence stems from our unique experiences, information, and even our personal biases. Understanding these subjective causal judgments is key to understanding why we make the choices we do.

But how can we decipher these hidden causal models? This article explores a fascinating approach: by observing our preferences over different actions. Inspired by the work of Judea Pearl, we'll delve into how our choices reveal the underlying causal relationships we believe in. By understanding these relationships, we can test whether our preferences are consistent with our causal beliefs and ultimately refine our decision-making processes.

What Are Causal Models and How Do They Work?

Surreal illustration of a mind with interconnected gears representing causal relationships.

At its core, a causal model is a framework for understanding how different factors in the world are interconnected. Imagine it as a map of cause and effect, where variables (like interest rates, unemployment, or consumer spending) are linked by equations that show how they influence one another. Some variables are 'exogenous,' meaning their values are determined by outside forces, while others are 'endogenous,' meaning their values are determined by the relationships within the model.

Causal models help us predict the consequences of our actions. By intervening in the system – say, by changing the interest rate – we can use the model to forecast how other variables will respond. However, it's crucial to remember that these models are subjective. Different individuals, with different information and experiences, will construct different models of the same situation. This subjectivity is what makes decision-making so complex and fascinating.

Here are some key aspects of Causal Models:

Variables: These represent different factors in the world, such as economic indicators, personal beliefs, or social trends.
Equations: These define the relationships between variables, showing how one variable influences another.
Exogenous vs. Endogenous Variables: Exogenous variables are determined by factors outside the model, while endogenous variables are determined within the model.
Interventions: These are actions that directly change the value of a variable, allowing us to see how the system responds.

The beauty of using causal models lies in their ability to make explicit our assumptions about the world. By laying out the relationships we believe to be true, we can identify potential biases and inconsistencies in our thinking. This allows us to refine our understanding and make more informed decisions.

The Future of Causal Decision Making

By making explicit the causal models that drive our choices, we can identify potential biases, refine our understanding, and ultimately make better decisions. As research in this area continues, we can expect even more sophisticated tools and techniques for decoding the hidden influences that shape our choices.

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.2401.10937,

Title: Subjective Causality

Subject: econ.th cs.ai cs.lo

Authors: Joseph Y. Halpern, Evan Piermont

Published: 17-01-2024

Everything You Need To Know

What exactly are 'causal models' and how do they impact our everyday decision-making processes?

Causal models are frameworks people use to understand how different factors in the world are interconnected, essentially acting as a map of cause and effect. Variables, like interest rates or personal beliefs, are linked by equations showing how they influence each other. The impact on decision-making is profound; by understanding these models, individuals can predict the consequences of their actions, identify potential biases, and make more informed choices. It's worth noting that causal models can be subjective, influenced by personal experiences and information, leading different people to construct different models of the same situation. Understanding how we construct these models is the first step toward improving our outcomes.

Can you explain the difference between 'exogenous' and 'endogenous' variables within a causal model, and how do they affect the model's predictions?

Within a causal model, 'exogenous' variables are those whose values are determined by factors outside of the model itself. They are the independent inputs. In contrast, 'endogenous' variables have values determined by the relationships defined within the model. They are the outputs or consequences within the system being modeled. The distinction is important because interventions (actions that directly change a variable's value) typically target exogenous variables to observe their effects on endogenous variables, allowing for prediction and analysis within the causal model.

How can we identify and refine our own 'causal models' to improve our decision-making?

Identifying and refining your own causal models involves a few key steps. First, make your assumptions explicit by laying out the relationships you believe to be true. This helps in identifying potential biases and inconsistencies in your thinking. Then, observe your preferences over different actions, as these preferences often reveal the underlying causal relationships you believe in. By testing whether your preferences are consistent with your causal beliefs, you can refine your understanding and make more informed decisions. Actively seek out new information and be open to revising your model based on evidence.

How do 'causal judgements' which are described as surprisingly personal, influence choices?

Causal judgments, because they are shaped by unique experiences, information, and personal biases, lead individuals to perceive cause-and-effect relationships differently. What one person sees as a direct consequence, another might view as a distant possibility. This divergence in understanding the causal structure of a situation directly influences choices because individuals act based on what they believe will be the outcome of their actions. Thus, subjective causal judgments are key to understanding why people make different choices, even when facing the same situation.

What are the limitations of relying on 'causal models' for decision-making, and are there situations where they might not be effective?

While causal models are valuable tools for decision-making, they have limitations. Their effectiveness hinges on the accuracy of the assumed relationships and the completeness of the variables considered. If the model omits key factors or misrepresents the influence between variables, the predictions can be flawed. Additionally, causal models can be subjective, reflecting individual biases and incomplete information. Situations involving high uncertainty, rapidly changing environments, or complex systems with many interacting factors may challenge the effectiveness of causal models. In such cases, it's crucial to complement causal modeling with other approaches, such as scenario planning or expert judgment, to make well-rounded decisions.