Ahmed T. Hammad
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A Causal Framework for Extremal Effects: Treatment Effects on Joint Extremes and Tail Dependence
Standard causal inference is built around the Average Treatment Effect. Did the treated group do better on average? By how much? This is a reasonable starting point, but itā¦
All About MLflow
If youāve spent any time doing machine learning seriously, youāve run into this problem: you trained a model last week that performed better than anything you have now, andā¦
Contextual Multi-Armed Bandit: Maximizing Rewards with Intelligent Decision-Making
Picture a row of slot machines. Each has its own payout probability, and you donāt know any of them upfront. Your goal is simple: walk away with as much money as possible.ā¦
Data Science Books
Books I actually recommend to people, with honest takes on what each one is good for.
Data Science project Boilerplate
Every time I start a new data science project, I go through the same setup steps. Create folders, set up the virtual environment, add a .gitignore, write the Dockerfile. Itā¦
Developing in a Docker container
I develop inside Docker containers. Not because itās trendy, but because it solves a real problem: my local machine stays clean, the project environment is reproducible, andā¦
Embracing Change: Incremental vs. Batch Machine Learning
Most machine learning tutorials train a model, evaluate it, and stop. Thatās fine for a homework assignment. In production, itās usually not how things work. Real systemsā¦
From ATE to Uplift Modeling
In a randomised controlled trial (RCT) the standard output is the
Average Treatment Effect (ATE)
: one number telling you how much the treatment moved the outcome on average.ā¦
How I Created R-Genius: A Journey into Empowering R Users with AI
R is one of the most powerful tools in data science. Itās also one of the most unforgiving. Error messages that reveal nothing, package ecosystems that overlap in confusingā¦
Logistic Regression and Marginal Effects
Logistic regression is everywhere in applied data science ā binary outcomes, classification problems, probability estimation. Most people know how to fit one. Fewer know howā¦
Machine Learning, Copula and Synthetic Data
Synthetic data is one of those ideas that sounds like it shouldnāt work ā if the data is fake, how can a model trained on it generalize to real data? The answer is thatā¦
On Learning Methods in the Age of AI
In recent years, something subtle has changed in how students approach programming. What used to begin with a blank script and a vague idea now often begins with a prompt. Aā¦
Online Uplift Modelling with River
The usual workflow is:
Probability Box with Kernel Density Estimation
Weather forecasts got me thinking about data. A simple historical table ā temperature, humidity, rain ā and the question: given all this data, what can I actually say aboutā¦
Quantile Random Forest
Most regression models give you a single number: the expected value of the target given the inputs. Thatās often what you want, but it throws away information. Quantileā¦
The 3 + 1 pillars of data science
A few weeks ago, one of my students posed a question I wasnāt expecting:
The Beauty of Soft Decision Trees
Decision trees work by making hard choices at each node: if
\(X_1 > 5\)
, go left; otherwise, go right. Itās clean, interpretable, and brittle. A point sitting right on theā¦
The R
rgcapi
library
Algorithmic trading has always interested me. The abundance of time-series data, the clear feedback loop, the challenge of building and testing strategies ā itās a domainā¦
Understanding Stationary: Concepts, Implications, and Approaches
Time series analysis runs through economics, finance, engineering, and the natural sciences ā any domain where observations are indexed in time and the ordering matters.ā¦
Unraveling the Power of Causal Machine Learning
Standard machine learning is very good at finding patterns. Given enough data, a model can identify that A and B tend to co-occur, that X is predictive of Y, that a certainā¦
Updating knowledge with Bayes
Iāve explained Bayesian updating many times over the years ā to students, to colleagues, to people with no statistics background at all. The examples I find work best areā¦
When in doubt, just model it. Modelling uncertainty
Thereās a pattern Iāve noticed across projects: when something is hard to measure, people tend to either ignore it or collapse it into a single number. Both choices feelā¦
memoire: Persistent Causal Memory for AI Coding Assistants
Every time you start a new session with an AI coding assistant, the same ritual plays out: you re-explain the project, the assistant re-reads files it already saw yesterdayā¦
pbox: Exploring Multivariate Spaces with Probability Boxes
In a previous post I introduced the idea of a āprobability boxā ā turning a dataset into a queryable probability space using Kernel Density Estimation. That was theā¦
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