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 [1], evaluating graph scores [2], or learning asymmetries in functional relationships [3][4]. 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, associated with work by Judea Pearl [5] and others, bring mathematical rigor and clarity to causal inference and allow for intervention analysis. However, they require upfront assumptions about the system structure. As a result, they may fail to scale or generalize, and depend heavily on expert input, which can introduce bias. 

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 and consistency 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. Simulations can be complex to build, sensitive to assumptions, and difficult to validate in real-world settings. 

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 are a novel metric that quantifies 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, not from model assumptions, which significantly reduces inductive bias. 

In line with the information theoretic approach of [6], 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. Because true causal relationships are asymmetric and non-reversible, these entropy differences serve as a robust signal of potential causal structure. 

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 (and their values) 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 without compromising the integrity of the analysis. 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 — a core feature for validating interventions or anticipating downstream effects. To further validate causal relationships, Howso supports counterfactual analysis — testing whether a discovered causal link would have held under different historical conditions. This retrospective capability strengthens confidence in causal insights by anchoring them in real-world variation, not just statistical correlation. 

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 

Uncertainty around the timing and causes of equipment failure often results in costly downtime and inefficient maintenance practices. Howso’s causal discovery can be harnessed to forecast when failures are likely to occur by identifying how anomalies and changes in key causal drivers signal impending issues. It also uncovers the root causes of failure — such as specific operating conditions, usage patterns, or environmental factors. This empowers teams to shift from reactive fixes to proactive strategies that address the underlying drivers, enhancing equipment reliability and minimizing operational disruptions. 

Supply Chain Optimization 

Inefficient or delayed store resourcing and rising operational costs can significantly disrupt business performance. Howso addresses this by identifying the causal factors that drive inefficiencies — such as supplier delays, stock imbalances, or inaccurate demand forecasts — and recommending optimal store inventory strategies grounded in data. These recommendations can be tested using hypothetical intervention analysis, allowing teams to simulate the impact of specific changes before implementation. This enables smarter, faster decisions across inventory and logistics scenarios, helping balance cost, availability, and service levels throughout the supply chain. 

Fraud Detection 

Slow and ineffective detection systems often miss nuanced or emerging patterns of fraudulent activity, leading to increased risk and delayed response. Howso enhances fraud detection by identifying and explaining the causal drivers behind anomalous behavior — such as unusual sequences of transactions, geographic inconsistencies, or atypical timing patterns. By uncovering what actually causes fraud to occur, rather than simply flagging correlations, Howso enables more accurate and proactive detection. These insights can also be used to simulate hypothetical interventions, helping organizations assess the impact of new fraud prevention strategies before deploying them. 

Advertising Effectiveness 

Identifying the ideal marketing segments — and justifying those choices — can be difficult without transparency into what’s driving campaign performance. Howso uncovers the causal drivers behind marketing outcomes, revealing why certain audience segments, channels, or messages perform better than others. By moving beyond surface-level correlations, Howso provides clear, explainable insights into what influences engagement and conversion. Marketers can test these findings through hypothetical intervention analysis to evaluate potential strategies before deployment, and use counterfactual testing to understand how alternative past decisions — such as different targeting or timing — might have changed campaign outcomes. This empowers more confident, data-backed optimization of marketing spend. 

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, informed decision-making. Howso’s unique approach combines rigorous data-driven discovery, transparent causal inference, and human-guided analysis to surface true drivers of outcomes. By uncovering the asymmetric relationships in the data, testing interventions before they happen, and validating findings through counterfactuals, Howso empowers teams to go beyond prediction — toward prescription and control. Whether optimizing operations, improving customer engagement, or reducing risk, Howso helps organizations harness the full power of causal reasoning to drive measurable, strategic impact. 

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References:

[1] Spirtes, Glymour, and Scheines (2000). Causation, Prediction, and Search. 

[2] Chickering (2002). “Optimal Structure Identification with Greedy Search.” 

[3] Shimizu et al. (2006). “A Linear Non-Gaussian Acyclic Model for Causal Discovery.” 

[4] F. N. F. Q. Simoes, M. Dastani, and T. van Ommen. Causal entropy and information gain for measuring causal control. In European Conference on Artificial Intelligence, pages 216–231. Springer, 2023. 

[5] Pearl, J. (2009). Causality: Models, Reasoning, and Inference. 

[6] F. N. F. Q. Simoes, M. Dastani, and T. van Ommen. Causal entropy and information gain for measuring causal control. In European Conference on Artificial Intelligence, pages 216–231. Springer, 2023. 

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