Name: Causality Engine
Price: 99 EUR
Availability: InStock
Rating: 4.8 (12 reviews)
Author: Causality Engine

Question 1

What is a causal inference platform?

Accepted Answer

A causal inference platform uses statistical and machine learning methods to determine cause-and-effect relationships from data, rather than just identifying correlations. The Causality Engine constructs directed acyclic graphs (DAGs) to map which variables cause changes in outcomes, identifies confounders and mediators, and validates causal claims through experimentation. This goes beyond traditional analytics which can only show that two variables move together, not that one causes the other.

Question 2

What is the difference between correlation and causation in data analytics?

Accepted Answer

Correlation means two variables move together. Causation means one variable directly causes a change in another. Most analytics tools, dashboards, and attribution platforms report correlations and present them as if they were causal. The Causality Engine uses causal discovery algorithms, directed acyclic graphs, and incrementality testing to prove actual cause-and-effect relationships. This distinction matters because acting on correlations can lead to wasted resources, while acting on causation leads to predictable outcomes.

Question 3

What industries can use causal inference?

Accepted Answer

Causal inference is domain-agnostic. Any industry with multivariate data and outcomes can benefit. The Causality Engine has applications in drug research (compound efficacy vs. confounders), sports optimization (training interventions that cause performance gains), supply chain (root causes of stockouts), manufacturing (process parameters that cause defects), finance and risk (causal drivers of drawdowns), energy and emissions (interventions that actually reduce emissions), waste processing (variables that cause sorting yield changes), and marketing attribution (channels that cause incremental sales).

Question 4

What is the Causality Audit?

Accepted Answer

The Causality Audit is a 90-day enterprise pilot engagement where the Causality Engine is deployed against your data to answer your highest-value causal question. It follows four phases: Data Ingestion and Domain Mapping (Week 1-2), Causal Discovery (Week 3-6), Validation through A/B tests and holdout experiments (Week 7-10), and Prescriptive Recommendations (Week 11-12). The pilot includes success criteria defined upfront, a full refund if criteria are not met, and 100% of the pilot fee credited toward an annual contract.

Question 5

How does the Causality Engine work?

Accepted Answer

The Causality Engine follows four steps: (1) Data Ingestion - any multivariate dataset from sensor feeds, transaction logs, clinical data, or ad spend; (2) Causal Model - the engine constructs a directed acyclic graph (DAG) of cause and effect, not a regression or correlation matrix; (3) Causal Graph - every relationship is quantified with X causes Y, Z is a confounder, W is a mediator, all scored for confidence; (4) Intervention - prescriptive recommendations stating if you change X by this amount, expect Y to change by this amount. The domain changes but the mathematical rigor does not.

Question 6

What is a directed acyclic graph (DAG) in causal inference?

Accepted Answer

A directed acyclic graph (DAG) is a mathematical structure used in causal inference to represent cause-and-effect relationships between variables. Each node represents a variable, and each directed edge represents a causal relationship. 'Acyclic' means there are no feedback loops. The Causality Engine automatically constructs DAGs from your data using causal discovery algorithms, identifying which variables cause changes in outcomes, which are confounders (common causes), and which are mediators (intermediate steps in a causal chain).

Question 7

How is causal inference different from machine learning?

Accepted Answer

Traditional machine learning excels at prediction (what will happen) but cannot answer causal questions (what causes what, and what happens if we intervene). A machine learning model can predict that customers who see a TikTok ad are more likely to buy, but it cannot tell you whether the ad caused the purchase or whether those customers would have bought anyway. Causal inference, as implemented by the Causality Engine, answers the intervention question: if we change X, what happens to Y? This is the difference between forecasting and understanding.

Question 8

Can the Causality Engine be used for drug research and clinical trials?

Accepted Answer

Yes. The Causality Engine can ingest patient cohort data, dosage information, biomarkers, and adverse event records to determine whether a compound causes a therapeutic effect or whether the observed effect is confounded by other variables. This enables faster Phase II signal detection and fewer false positives by separating true causal drug effects from spurious correlations in clinical data.

Question 9

How does causal inference apply to supply chain optimization?

Accepted Answer

In supply chain management, the Causality Engine ingests lead times, inventory levels, demand signals, and disruption events to identify the root causes of stockouts and cost overruns. Rather than reacting to symptoms, the platform identifies what actually causes disruptions, enabling preventive action. This includes causal demand forecasting that accounts for true drivers rather than correlated signals.

Question 10

What data does the Causality Engine need?

Accepted Answer

The Causality Engine works with any multivariate dataset that has variables and outcomes. This includes sensor feeds, transaction logs, clinical trial data, advertising spend data, production line sensors, energy consumption records, financial market data, and more. Data can be ingested via APIs, CSV uploads, or direct database connections. There is no requirement for a specific SDK or pixel installation, and no 90-day onboarding period.

Your dashboards show what happened.
They have no idea why.

One engine. Any question. Any industry.

Four steps. Zero guesswork.

Data In

Causal Model

Causal Graph

Intervention

The Causality Audit.

Plug In

Discover

Validate

Prescribe

Your tools guess.
We do the math.

Your dashboards show what happened.They have no idea why.