What Is Causal AI?

Causal AI allows data scientists to examine root causes leading to specific events. This differs from most machine learning algorithms, which operate by predicting an outcome from patterns they are trained to recognize. We share how causal AI compares with other AI models.

Correlation AI

Correlation AI is a statistical model that makes predictions based on the dataset it has learned. While it can predict possible outcomes based on an input data point, correlation AI cannot determine the precise causal factors of specific events. Moreover, correlation AI might be biased because of the nature of the training data it learned from.

In contrast, causal AI can confidently unravel complex cause-and-effect relationships of events. A causal AI model doesn't make assumptions but predicts based on facts and observations. With causal reasoning, the model can visualize the path it took to arrive at a specific prediction.

Generative AI

Generative AI consists of large language models capable of producing unique content by learning from massive datasets. Unlike causal AI, generative AI cannot establish the unknown causal relationships between variables. Generative AI, like other purely predictive models, operates by inferencing the most likely answer based on the probabilistic likelihood of the given prompt.

Meanwhile, causal AI models infer based on evidence discovered when examining historical data. Causal AI makes no assumptions but studies the relationship between data points to model them accurately. Unlike generative AI, causal models are interpretable, where every decision can be traced to a deterministic factor.

Explainable AI

Interpreting the results that deep learning algorithms generate is nearly impossible because of their black-box-like architecture. With explainable AI, data scientists can apply explainable techniques like Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) to assess how influential the model's feature is when making predictions.

Explainable AI tries to explain why it reaches a specific outcome after making such predictions. Conversely, causal AI applies explainability at the early stage of the model and is more transparent. Every decision is visible to observers from the start.

Causal AI allows data scientists to go beyond regression and probabilistic modeling. It works by systemically inspecting factors that precede a decision in the observational data. A causal model must discover possible deviations from the gathered information to do that.

Then, data scientists create structural causal models to visualize the relationships of root causes and their corresponding outcomes. They also incorporate domain expertise to increase the accuracy of their finding and optimize interventions. Data scientists apply techniques like causal discovery, fault tree analysis, and causal inference to infer causality from the data with substantial precision.

Causal discovery algorithms

Causal discovery is the process of identifying possible relationships between data points and representing them with a causal graph. A causal graph is a diagram that describes how one variable influences the other. Data scientists use score-based and constraint-based discovery algorithms to create a causal graph.

Score-based

Score-based causal discovery assigns a confident score to a directed causality graph (DAG), the simplest causal graph data scientists use to analyze causal relationships. This approach enables accurate discovery of unknown variables influencing both cause and effect. Below are several causal discovery algorithms developed from the score-based approach.

• Greedy Equivalence Search (GES) returns the highest score by optimizing the score functions in the forward and backward phases.

• Greedy Interventional Equivalence Search introduces a turning phase to extend the GES and improve modeling accuracy.

• The Fast Greedy Equivalence Search functions similarly to GES, except it speeds up the estimation by computing the data in parallel.

Constraint-based

Constraint-based causal discovery assumes that specific causal factors somehow relate variables in a dataset. The method chooses causal models that fit onto all independent conditions or constraints. Generally, constraint-based approaches are more efficient than score-based methods but are easily influenced by error.

Fault tree analysis

Some causal AI models apply faulty tree analysis to determine causality. Fault tree analysis is a logical technique that allows analysts to trace a failure point to its root cause. It starts by mapping the problem as a node and sequentially branches to intermediate and fundamental causes in a top-down approach.

Causal inference

Causal inference is a discipline for evaluating the weights that certain variables carry in causal relationships. Data scientists measure the causal effects they observe from experimental and historical data to validate their assumptions. They use their findings to understand and predict how various interventions affect outcomes differently.