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← Causal Inference 101

5 min read

Causal inference in fintech & wealth management

Incrementality for coaching, advisor outreach, and product nudges — when wealthy clients would have grown anyway.

Banks, fintech, and wealth platforms often ask whether advice programs work — not which clients were already saving more or growing AUM. Correlation is misleading when premium clients receive more outreach.

Causal methods estimate incrementality: did coaching or advisor contact cause better 12-month savings growth or AUM growth, after adjusting for income, prior balances, and tenure?

Questions that fit CausalLens

  • Did financial coaching cause higher savings growth (retail banking / wellness)?
  • Did proactive advisor outreach cause incremental AUM growth (wealth management)?
  • Which client segments respond most to outreach (causal forest / T-learner)?

What this is not

CausalLens is not for stock picking, factor attribution, or portfolio alpha research. It is for program and product evaluation when you cannot randomize who receives advice or coaching.

Run this method on your data — no Python

CausalLens runs matching, DiD, causal forests, DoWhy refutation, and more — with balance tables, sensitivity checks, and PDF export.

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