# Howso: Causal AI and Causal Discovery

Bridgette Befort DeFever, PhD  
April 17, 2025

###### Introduction

Across industries, organizations strive to understand the key drivers of their outcomes — not just to observe trends, but to take meaningful action. Whether the goal is improving patient treatments, optimizing industrial processes, or forecasting sales, identifying the features that influence results is essential for effective interventions and informed decision-making.

Despite the abundance of analytical tools available, organizations still struggle to find solutions that go beyond recognizing patterns or making predictions. The ability to reveal both _why_ something is happening — to isolate the true causal factors — and _what to do_ about it remains elusive, and yet it is central to strategic, confident decisions.

As analytics strategies evolve from predictive to prescriptive, uncovering and validating causal relationships becomes a critical capability. This is where Howso stands apart. Unlike other tools that are inflexible or limited in their ability to uncover causal structure, Howso offers a data-native, scenario-focused, and human-guided approach to causal discovery. Its techniques allow users to explore relationships directly from their data, validate findings transparently, and apply those insights in real-time decisions.

This blend of explainability, flexibility, and decision support makes Howso uniquely positioned to power a new generation of data-driven decision intelligence.

###### Background: Existing Approaches to Causal AI and Causal Discovery

Organizations that want to understand the “why” behind outcomes face a range of causal discovery approaches, each with its own assumptions, capabilities, and limitations. Broadly, these approaches can be grouped into four categories: data-driven discovery, model-based reasoning, hybrid and domain-enhanced systems, and simulation-centric tools.

_Data-Driven Discovery_

These techniques infer causal structure directly from observational data using statistical or machine learning methods. Common strategies include detecting conditional dependencies, evaluating graph scores, or learning asymmetries in functional relationships. Data-driven methods work well with large datasets and can be highly automated, making them appealing for exploratory analysis.

However, they can also generate false positives, especially in the presence of noise or unobserved confounders. Their conclusions are limited to the features available in the data, and results may be opaque or unreliable in smaller samples.

_Model-Based Reasoning_

Model-based approaches begin with formal representations — often directed acyclic graphs (DAGs) or structural causal models (SCMs) — and use logic or calculus to reason about cause and effect. These techniques bring mathematical rigor and clarity to causal inference and allow for intervention analysis. However, they require upfront assumptions about the system structure, which may fail to scale or generalize and depend heavily on expert input.

_Hybrid and Domain-Enhanced Systems_

Hybrid approaches combine algorithmic techniques with human knowledge, such as expert rules, domain context, or natural language inputs. This integration can help refine models and capture real-world nuance, especially when data is sparse or incomplete. However, these systems often require manual configuration, may not produce a fully coherent causal model, and are subject to the quality of the human input they rely on.

_Simulation-Centric and Counterfactual Tools_

Simulation-based approaches explore “what if” scenarios to test the downstream effects of potential changes. These tools help surface possible causal pathways by evaluating how interventions play out under different conditions. While valuable for scenario testing, these methods do not necessarily uncover true causal relationships and can be complex to build.

###### Howso’s Approach to Causal AI and Causal Discovery

Howso combines the strengths of several causal discovery paradigms — data-driven analysis, human-in-the-loop refinement, and simulation-based validation — into a distinct, flexible, and transparent approach. Unlike tools that are narrowly academic or rigidly automated, Howso is built for enterprise teams who need causal insights they can trust, test, and act on. It is a data-native, human-guided, and scenario-focused causal reasoning tool, purpose-built for decision intelligence in real-world environments.

_Data-Driven Causal Discovery_

At its core, Howso is powered by a proprietary form of instance-based machine learning (IBL). Unlike traditional modeling approaches, IBL stores each data point (instance) and makes predictions by comparing new inputs to these known instances. Howso’s implementation is made practical and scalable through a fast spatial query engine and a novel information-probability space kernel — enabling efficient reasoning across large, high-dimensional datasets.

This architecture enables Howso to compute uncertainty estimates (measured in mean absolute error, or MAE) for each feature of each data point. Using robust sampling across the power set of feature combinations, accuracy contributions are calculated across all features. Accuracy contributions quantify how much the presence of one feature reduces the uncertainty in predicting another. These values measure directional influence between features in a purely data-driven way, significantly reducing inductive bias.

In line with the information theoretic approach, which identifies causal relationships based on the asymmetries in information content (i.e., entropy) between two features, Howso translates the accuracy contributions into entropy values. The differences in entropy, or asymmetric relationships between features, represent the edges of a causal graph, where directional influence (i.e., causality) flows from one feature to another.

_Augmenting Discovery with Human Insight and Scenario Analysis_

Once a causal network is constructed, analysts and subject matter experts (SMEs) can interact directly with the data to explore, test, and validate insights. Howso supports flexible _conditioning_, allowing users to deep dive into causal drivers of specific features in the data. SMEs can direct the analysis by specifying which relationships or scenarios to investigate more deeply, bringing human judgment and domain knowledge into the loop. If a potential causal relationship is surfaced, it can be interrogated further using what-if simulations. These scenario-based tests help quantify how changes in one feature are likely to impact others. To further validate causal relationships, Howso supports counterfactual analysis — testing whether a discovered causal link would have held under different historical conditions.

###### From Insight to Action: Real World Application

The ultimate value of causal reasoning lies in its ability to inform decisions. Howso’s analysis doesn’t stop at explanation — it enables prescription, for true decision intelligence. Users can explore not just what drives outcomes, but what to do about them. Whether adjusting operational parameters, marketing spend, or resource allocations, Howso helps teams translate causal insights into clear, data-backed strategies. Below are four high-impact examples where Howso’s approach adds unique value:

_Predictive Maintenance_

Howso’s causal discovery can be harnessed to forecast when equipment failures are likely to occur by identifying how anomalies and changes in key causal drivers signal impending issues. This empowers teams to shift from reactive fixes to proactive strategies focusing on underlying drivers.

_Supply Chain Optimization_

Howso addresses inefficiencies by identifying the causal factors that drive disruptions and recommending optimal store inventory strategies grounded in data.

_Fraud Detection_

Howso enhances fraud detection by identifying and explaining the _causal drivers_ behind anomalous behavior, enabling more accurate and proactive detection.

_Advertising Effectiveness_

Howso uncovers the _causal drivers_ behind marketing outcomes, providing clear insights into what influences engagement and conversion.

###### Conclusion

In a data-driven world, understanding what is happening is no longer enough — organizations need to understand why outcomes occur and what to do about them. Causal discovery fills this critical gap, enabling a shift from reactive analytics to proactive decision-making. Howso’s approach combines rigorous data-driven discovery, transparent causal inference, and human-guided analysis to surface true drivers of outcomes, empowering teams to drive strategic impact.