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