Causal Machine Learning: What It Is, Why It Matters, and How It Works

Published on

May 19, 2026

Introduction

Commonly used machine learning and deep learning algorithms learn correlations between data. These models are extremely powerful and are being used in different areas like computer vision, natural language processing, and forecasting.

Nonetheless, correlation does not imply causation. Take, for instance, a hot summer day [1]. It is very likely that energy demand might increase. It is also possible that ice cream sales surge. These two variables are correlated, or in simpler terms, “in sync”. However, neither is the energy demand driving ice cream sales, nor is it the other way around.

Of course, this example is fairly simple to detect. The temperature rise is what causes both the increase in ice cream sales and energy demand. In the context of causal inference, temperature acts as a confounder when we are trying to understand whether there is a causal effect between energy demand and ice cream sales.

*Figure 1: Example of a confounder: Temperature rise causes both an increase in energy demand and ice cream sales.*

In other cases, it is less obvious. Take, for instance, the development of drugs. Here, it is crucial to determine whether the patients healed because they took the medicine or whether other factors were at play. In this case, randomized A/B tests are generally used to determine causality. However, in many real-world settings, this approach is not feasible, ethical, or affordable. We cannot always randomly assign people to different treatments, business decisions, policies, or environmental conditions just to observe what happens.

In these cases, we often need to work with observational data which was not collected in a controlled experiment and this makes causal analysis more difficult. In addition, these situations often involve large and complex datasets, with many nonlinear interactions between variables, which is where machine learning shines.

Here is where causal machine learning becomes relevant. But, what is Causal ML exactly? It’s an umbrella term for approaches that explicitly incorporate causal assumptions into machine learning workflows. This allows us to understand the cause-and-effect in certain processes and situations, and to build more robust models. While traditional machine learning is often excellent at forecasting outcomes, causal ML focuses on understanding interventions (i.e. what happens when we actively change something), counterfactuals (i.e. what would have happened under a different decision or condition), and the mechanisms behind observed data.

Causal machine learning: Fundamentals, applications and limitations

Causal graph

One of the key features of causal machine learning is that assumptions of what variables influence each other are explicitly defined. These are represented via directed acyclic graphs (DAGs), which are also known as causal graphs. These graphs, as the name suggests, should not contain any cycle, so a variable cannot eventually cause itself through a loop as such:

A → B → C → D → A

In simple terms, DAGs provide the causal map behind the data. They guide the machine learning model by clarifying what needs to be adjusted for, what should not, and which assumptions are being made.

Example use case

One use case for causal ML is invariant feature learning. Let’s take image classification for different species as an example. Many animals are commonly found in a specific habitat, for instance, cows in pastures. If the dataset contains only pictures with that background, the model might fail to recognize the cow outside its natural habitat. In other words, the model learned the spurious association between the surrounding pasture and the label [2].

The directed acyclic graph for this situation would be the following [3]:

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Figure 2: Directed acyclic graph for species classification: The background and species make the input image. If too many samples feature one species with the same background, the model might learn the spurious association between the background and the labels

To solve this, one approach is to use "generative interventions" [3]. Instead of collecting more biased data, they steer generative AI models to actively manufacture interventions on the images, such as changing camera viewpoints, removing backgrounds and altering scene context. By training on these intervened images (plus the original ones), the model is forced to stop relying on the background. Instead, it focuses on the true causal features of the object, such as its physical shape.

To learn more about other use cases of causal machine learning, such as causal supervised learning, causal explanations and causal fairness, we refer to “Causal Machine Learning: A Survey and Open Problems “ [2].

Limitations

As stated previously, one of the key characteristics of causal machine learning is that causality assumptions are baked into the models. Making these assumptions can result in bias amplification and harming external validity compared to purely statistical models.

Another one is the lack of ground-truth evaluation data. In order for the models to be benchmarked, interventional data (such as randomized control trials) is required. Generating it involves interacting with the environment, which can be expensive, unethical or time consuming, compared to passively observed datasets like those used to train LLMs or other predictive deep learning models [2].

Conclusions

As discussed in this article, causal machine learning seeks to amplify machine learning techniques by incorporating true cause-and-effect relationships between variables. This feature can be very helpful in various scenarios, such as diagnostics or cases where data drift might occur.

It, however, comes at the cost of stronger modeling assumptions than other statistical methods and predictive machine learning approaches.

Table 1: Comparison between predictive machine learning and causal machine learning

Aspect	Predictive machine learning	Causal machine learning
Main goal	Predict what is likely to happen based on existing patterns.	Estimate what would happen if something changed, or learn the true underlying mechanisms.
Typical question	“What will the outcome be?”	“What is the effect of this intervention?”
Focus	Correlations and patterns.	Cause-and-effect relationships (true core features).
Output	Predictions, scores, classifications, forecasts.	Treatment effects, intervention effects, counterfactual estimates, robust classifications.
Data used	Historical data, usually observational (e.g., passively collected image datasets).	Observational data, experimental data, or generated interventions (e.g., using AI to simulate altered backgrounds).
Key assumption	Future data will behave similarly to past data (training distribution matches test distribution).	The causal structure is correctly specified or reasonably assumed via a causal graph.
Business use case	Forecast demand, classify users, detect anomalies, and standard image recognition.	Estimate the impact of pricing/policies, robust image recognition (generalizing to new, unseen environments).
Limitation	Fails when spurious correlations break in the real world .	Requires stronger assumptions, careful problem formulation, or complex methods to simulate interventions.

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