Your Neural Network Can’t Explain This. TMLE to the Rescue!

Targeted Maximum Likelihood Estimation (TMLE) help you explain patterns where other techniques fall short

When your Neural Networks keep you in free fall, TMLE might be your parachute. Image generated by Leonardo AI

Neural networks can spot patterns, correlations, and trends with stunning accuracy. But when it comes to answering 'Why did this happen?' they’re as clueless as a parrot mimicking human speech.

They’ll give you predictions, sure—but try asking them for an explanation, and you’ll be left staring at a black box.

This limitation isn’t unique to neural networks. Correlation-based methods like linear regression and even advanced tools like Propensity Score Matching cannot get to the core of causation-based trends in complex data. That is a problem when decision-makers (read: your managers) demand actionable business insights and not geeky stats that only make nerds happy.

At risk of contradicting myself, here’s a very geeky subject for you: Targeted Maximum Likelihood Estimation (TMLE). The thing is, TMLE is the best of both worlds. It lets you play around with numbers as much as your nerdy brain desires, but it also makes your managers happy by producing business insights.

Essentially, you get the rigor of causal inference plus the flexibility of machine learning, This allows you to identify why something happened—not just what happened. The acronym is a bit of a mouthful, but TMLE really helps. Even with messy real-world data.

By the end of this article, you’ll have understood:

• The core principles behind TMLE and what makes it unique.

• How to implement TMLE step-by-step in Python.

• Real-world applications that demonstrate its power, from sustainability analysis to healthcare and beyond.