What is causal AI?
Causal AI is a form of artificial intelligence (AI) designed to identify and understand the cause and effect of relationships across data.
It surpasses traditional machine learning and other AI models’ ability to move beyond correlation to establish cause-and-effect relationships.
Large language models and generative AI rely on recognizing patterns in data, offering numerous benefits. However, their limitations have become increasingly evident. For example, they tend to “hallucinate” or generate false information and are unable to establish cause-and-effect relationships.
In contrast, causal AI employs causal inference. This method identifies the root causes of events, providing a deeper understanding of the underlying mechanisms.
This groundbreaking approach, rooted in Judea Pearl’s research on causality, enables causal AI to analyze existing data and model accurate hypothetical scenarios.
By employing techniques like causal discovery algorithms and structural causal models, causal AI can determine an event’s independent effect and generate accurate responses to queries about the impact of changing variables.
This deeper level of explainability and reduced bias holds immense potential for various applications. From healthcare to finance, where understanding the underlying causes of events is crucial for informed decision-making.
How does causal AI work?
Causal AI utilizes causal inference and fault tree analysis to model cause-and-effect relationships between variables and events in observational data. This data-driven approach goes beyond traditional machine learning. By enabling the construction of causal models that explain the mechanisms that drive outcomes.
It employs two critical methodologies:
- Causal discovery: Algorithms analyze data patterns to uncover potential causal relationships and construct models representing cause-and-effect dependencies between variables.
- Structural causal models: These models incorporate domain expertise to refine understanding of causal mechanisms. Additionally, they estimate the effects of interventions through hypothetical scenarios and counterfactual analysis.
A practical example of causal AI is fault tree analysis. A top-down approach using Boolean logic to trace the events leading to system failures. This method pinpoints root causes by mapping relationships between component failures and system malfunctions.
Here’s how causal AI works in practice:
- Data collection: Gather extensive observational data capturing relevant events, behaviors, and metrics.
- Causal discovery: Algorithms identify potential causal relationships within the data.
- Causal model building: Construct models like Bayesian networks or structural causal models to represent the discovered causal dependencies.
- Domain expertise integration: Refine causal models by incorporating insights from domain experts.
- Causal effect estimation: Use counterfactual analysis to estimate the impact of hypothetical interventions.
- Intervention testing: Simulate or conduct small-scale tests of potential interventions to predict their effectiveness.
- Model iteration: Continuously refine models as new data becomes available to enhance accuracy and provide ongoing explanations.
By leveraging causal inference and domain knowledge, causal AI offers a powerful tool for understanding the underlying drivers of events and predicting the outcomes of potential interventions.
How causal AI handles missing data?
Causal AI needs accurate and complete data to work reliably. When some data is missing, it can use these strategies:
- Data imputation: Different methods can be used to estimate the missing values based on the patterns found in the existing data. Some common methods to predict the missing values include using:
- the most frequent value
- calculating averages
- using machine learning models
- Multiple imputation: This method creates several versions of the filled-in data, each with different plausible values for the missing information. These versions are then combined to give a more complete and accurate picture.
- Inverse probability weighting: This technique accounts for missing data by changing the importance of the existing data points. It gives more weight to data points similar to the ones with missing information. Making it more reliable.
How causal AI differs from and improves on other AI?
Causal AI offers distinct advantages and benefits over other forms of AI tailored to specific use cases:
- Deeper explainability: Causal AI goes beyond correlations to identify the root causes behind outcomes, providing a more comprehensive understanding of complex systems.
- Reduced bias: By uncovering true causal relationships rather than relying on surface-level correlations, causal AI mitigates biases in machine learning datasets, leading to more accurate and equitable outcomes.
- Hypothetical question answering: Data scientists can use causal AI to answer “what-if” questions, estimating the effects of potential interventions before implementing them in real-world scenarios.
- Improved optimization: Causal AI goes beyond just predictive analytics. Enabling applications like root cause analysis and scenario planning leads to enhanced optimization and decision-making across various domains.
Real-world applications
In business, it can help understand customer churn and transaction decline to improve retention and conversions.
- Healthcare – It can reveal the causal effects of treatments, aiding in selecting optimal interventions.
- Finance – Financial institutions can use it to analyze the root causes of investment risks and mitigate potential losses.
- Fraud detection – Causal AI also plays a crucial role in fraud detection by identifying the events and conditions that enable fraudulent activities, leading to more effective prevention measures.
- Manufacturing – In manufacturing, it can be used to analyze the root causes of production line failures and optimize supply chains.
- Government – Even government bodies can leverage causal AI to evaluate the potential impact of policies through simulations, promoting evidence-based decision-making.
Benefits for businesses
Causal AI offers numerous benefits and applications for businesses to leverage. Here are several key areas where it can provide value:
- Bias removal: By uncovering true causal relationships, causal AI mitigates biases, leading to more accurate insights and decision-making.
- Customer journey optimization: It scrutinizes customer journeys to pinpoint the root causes behind low conversion rates or abandonment at various touchpoints, enabling businesses to optimize these interactions.
- Churn prevention: By identifying factors driving customer churn, causal AI models can simulate interventions to determine the most effective strategies for enhancing customer retention.
- Human resources: It analyzes factors contributing to employee turnover and provides actionable recommendations to improve retention rates and overall employee satisfaction.
- Investment risk analysis: It helps understand the causal mechanisms behind investment risks, allowing businesses to develop effective mitigation strategies and capitalize on new opportunities.
- Business operations optimization: By simulating changes to pricing, promotions, and marketing strategies, businesses can utilize causal AI to estimate the impact of these adjustments on crucial metrics, leading to better-informed decisions and improved strategy.
- Understanding root causes: It provides valuable explainability by revealing the underlying root causes of business events and outcomes, empowering businesses to make more informed and targeted decisions
What is the future of causal AI?
As organizations increasingly adopt AI, the demand for causal AI, which can identify root causes and model potential outcomes, is expected to rise.
Gartner recognized causal AI as a significant emerging technology, underscoring its growing importance.
