Causal literacy is the ability to learn what affects what from imperfect evidence, using reasoning, and seeking opportunities to improve the evidence. And the habit of doing so.
What is Causal Literacy?
Causal literacy is the ability to:
- assess how one action or approach affects outcomes, relative to an alternative.
- understand how multiple causes and effects interact to produce “system-wide outcomes”.
in personal, professional, and societal contexts.
Causal literacy includes the ability and habit to:
- use evidence from diverse sources, from rigorous research to everyday observations.
- reason logically and explore additional causes and effects
- identify what additional evidence would improve our assessment—and opportunities to create such evidence.
- recognize when useful evidence is unavailable or unattainable.
Borrowing language from the definition of quantitative literacy by Lynn Arthur Steen, causal literacy is “an aggregate of skills, knowledge, beliefs, dispositions, habits of mind, communication capabilities, and problem solving skills that people need in order to engage effectively in” causal situations and decision-making “arising in life and work.”
Learning Goals of Causal Literacy
(Click to open examples)
Distinguish between describing what is and estimating what would happen if something changed.
Example:
What is: On average, private school students have higher test scores than public school students.
What-if question: What would the average test scores of those same public school students be if they attended private schools instead?
Appreciate that changes produce effects through intermediate steps, explore possible pathways through which those effects occur, and identify potential weaknesses in those pathways.
Example:
Health advocates suggest nutrition education in schools to reduce childhood obesity. The program would teach what constitutes healthy eating and the health consequences of different food choices. As they examine the causal pathway, they recognize that the program would have to produce substantial changes in children’s eating habits to reduce obesity. Identifying these potentially weak causal links, they consider expanding the program to address enjoyment and convenience.
For a particular decision, identify the change and outcome that matter, and frame the question as estimating the effect of that change on that outcome.
Example:
An organization is deciding between a detailed and a streamlined approach to employee performance evaluations. It identifies employee morale and job performance as the outcomes they care about that are most likely to be affected. It then frames the key question: What would be the effect of using a streamlined rather than a detailed approach on employee morale and job performance? Because the answer cannot be known with certainty, it seeks to estimate the effect using the best possible evidence and logical reasoning.
Develop the habit of considering and investigating alternative explanations for differences in outcomes across individuals or groups.
Example:
One hospital has lower surgical complication rates than another. Before concluding that one hospital provides better care, investigators consider other explanations, such as differences in patient health, the types of procedures performed, or how complications are measured.
Develop the habit of considering and investigating alternative explanations for changes in outcomes over time.
Example:
An organization changes its scheduling software, and appointment no-shows increase. To assess whether the new software caused some or all of the increase, it identifies other factors that could affect appointment attendance and determine whether any of those factors also changed.
Recognize why estimating what would happen if something changed is often difficult.
Example:
A person had increasing trouble sleeping after starting a new medication. During the same period, the weather became much hotter, and they started a new job. Any of these changes—or a combination of them—could have contributed. They should investigate each possibility, recognizing that it may not be possible to determine how much each factor contributed.
Recognize that multiple causes and effects can interact in ways that reinforce, offset, or otherwise influence one another, producing system-wide outcomes.
Example:
A company asks employees to respond to emails within one hour. Response times improve initially. People begin sending more emails as they expect rapid responses. Responding quickly crowds out other work and employees become increasingly unable to maintain the one-hour response time. The increased email volume undermines the original benefit and may even reduce overall productivity.
Isn’t Causal Literacy Just Critical Thinking?
Yes and no.
The VALUE Rubrics define critical thinking as “a habit of mind characterized by the comprehensive exploration of issues, ideas, artifacts, and events before accepting or formulating an opinion or conclusion.”
In many contexts, critical thinking requires causal literacy. To think critically about policies, programs, medical treatments, personal decisions, or social issues, we must reason about causes and effects using evidence. If you’re not convinced yet, see the Case for Causal Literacy.
But causal reasoning is not just another part of critical thinking. It requires a distinct set of skills to accurately draw causal conclusions. These skills are rarely taught explicitly and seldom develop through experience alone.
Writing (communication) and quantitative literacy each have their own VALUE Rubrics and educational movements because they require both explicit instruction and integration across the curriculum. Causal literacy deserves the same investment.
Causal Inference for the Many
The causal literacy learning goals are, in essence, learning goals for causal inference. In recent decades, the field has made great strides, including potential outcomes, natural experiments, and causal diagrams.
These concepts are usually taught in advanced, technical ways. Yet many of their core ideas—and the practical skills they support—can be taught and applied in everyday, non-technical ways. I started with the most critical and accessible learning goals, but I extend them below.
(My primary focus is on causal inference—estimating the effect of a single change relative to an alternative. Causal literacy also includes complex causality—the ability to recognize causal patterns, such as feedback loops, and predict system-wide effects. That topic is important, but not the main focus on this site, as I discuss .)
I believe the learning goals I listed above can—and should—be accessible to everyone, even beginning in middle school.
But I have some chutzpah in making that claim, because my teaching experience is largely with master’s students, especially master’s in public administration (MPA). Still, many of my students describe themselves as “not at all quantitative,” so I suspect they’re more representative of the general population than one might expect.