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 does an enterprise engagement with Causality Engine actually deliver?

Accepted Answer

An enterprise engagement runs causal inference on the corporate dataset of the customer's choice and surfaces the operational dynamics their existing analytics stack cannot see. Beyond marketing attribution, this covers supply chain root cause, churn causation vs co-symptom, manufacturing yield drivers, fraud pattern causality, pricing elasticity controlling for confounders, employee retention causation, and any other multivariate operational question. The deliverable is a quantified causal graph with confidence and sensitivity bounds, plus prescriptive interventions the COO or Chief Data Officer can act on in the next budget cycle.

Question 4

What industries can use causal inference?

Accepted Answer

Causal inference is domain-agnostic. Any operation with multivariate data and outcomes can benefit. The Causality Engine has run engagements across marketing attribution, supply chain operations, manufacturing yield, customer churn and retention, fraud detection, pricing and demand elasticity, workforce attrition, drug research, sports performance, energy and emissions, and waste processing. The math does not care about the vertical; what matters is whether the operational question is causally identifiable from the available data.

Question 5

What is the Causality Audit?

Accepted Answer

The Causality Audit is a 90-day enterprise pilot engagement where Causality Engine runs causal inference on the customer's chosen dataset to answer one high-value operational question. It follows four phases: ingest the data on the customer's security terms (VPC, read-only replica, or anonymized export) and map the candidate causal graph (Week 1-2), discover causal relationships separating signal from correlation noise (Week 3-6), validate every claim with A/B experiments and holdout groups (Week 7-10), and deliver prescriptive recommendations with confidence and sensitivity bounds (Week 11-12). Success criteria are defined upfront in writing. On success, 100% of the pilot fee credits toward an annual contract. If criteria are missed, the engagement closes there with no annual conversion and no extension; the buyer is not locked into anything beyond the pilot fee.

Question 6

Where does Causality Engine run on enterprise data?

Accepted Answer

Three integration patterns, picked in the kickoff. (1) VPC / private cloud: the engine deploys inside the customer's AWS, GCP, or Azure VPC; data never leaves their perimeter; audit logs flow into their SIEM. (2) Read-only replica: the customer provisions a read replica of the relevant tables; we connect through their existing VPN or IP-allowlisted endpoint; zero write access. (3) Anonymized export: the customer ships a hashed-key, aggregated CSV or Parquet; the model runs causally on the structure without ever seeing identifiable rows. Data residency defaults to EU primary, with regional placement on request. TLS 1.3 in transit, AES-256 at rest, SOC 2-aligned controls, GDPR compliant.

Question 7

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 8

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 9

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 10

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 11

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 12

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.

The operational dynamics
your stack cannot see.

The questions your existing tools cannot answer.

One engine. Any dataset. Any operational question.

Four steps. Your data stays where it is.

Your dataset

Causal model

Causal graph

Intervention

The Causality Audit.

Ingest

Discover

Validate

Prescribe

We adapt to your security review, not the other way around.

Tell us the question.
We will tell you if it is causally answerable.

Joris van Huët

The operational dynamicsyour stack cannot see.