Causal inference that estimates real effects, not just correlation
Causal Labs applies causal inference — econml, DoWhy, double machine learning and causal forests — to estimate real causal effects from messy, real-world financial data, going beyond correlation-based analytics. Delivery has included a causal pipeline with confounder discovery, double machine learning, counterfactual estimation and debiasing validation gates.
Causal Labs applies causal inference — econml, DoWhy, double machine learning and causal forests — to estimate real causal effects from messy, real-world financial data, going beyond correlation-based analytics. Delivery has included a causal pipeline with confounder discovery, double machine learning, counterfactual estimation and debiasing validation gates.
Why correlation-based analytics leads to the wrong decision
Most business analytics answers 'what happened' — dashboards, correlations, trend lines. The decision that actually matters is 'what would happen if we changed X', and correlation cannot answer that reliably in the presence of confounders. Acting on a correlation that isn't causal means optimizing for the wrong lever.
How we approach a causal inference engagement
Requirements gathering
We identify the specific causal question — the intervention and the outcome — rather than starting from available data.
Build
Confounder discovery, double machine learning or causal forest estimation, and counterfactual analysis matched to the data's actual structure.
QA & review
Debiasing validation gates check the estimate holds under different specifications before it's presented as a result.
What's included
Confounder discovery
Systematic identification of variables that could bias a naive correlation into looking causal.
Double machine learning
Debiased estimation of treatment effects from observational, non-experimental data.
Counterfactual estimation
Answers to 'what would have happened' questions grounded in the causal model, not extrapolation.
Validation gates
Sensitivity checks that flag when an estimate isn't robust to reasonable alternative specifications.
- Causal analysis pipeline
- Confounder discovery methodology
- Effect estimates with validation gates
- Written interpretation of results
Microsoft's causal ML library, used for heterogeneous treatment effect estimation.
Provides the causal graph and identification framework underlying the analysis.
The estimation methods used to debias observational effect estimates.
Built a causal pipeline with confounder discovery, double machine learning, counterfactuals and debiasing validation gates.
typical delivery window
Questions about this service
How is this different from standard A/B testing?
A/B testing gives you a causal answer when you can randomize. Causal inference methods like double ML are for exactly the cases where you can't — historical, observational data where randomization already happened, or never will.
Making a decision you'd rather back with a real causal estimate than a correlation?
Tell us the question you're trying to answer and what data you have.
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