You will get Causal Machine Learning & Counterfactual Data Analysis
Project details
Standard machine learning predicts correlation, not causation. If you need to evaluate interventions, answer "what-if" scenarios, or generate counterfactual recourse for algorithmic decisions, predictive ML alone will fail due to confounding bias.
Causal Machine Learning combines structural causal inference with modern AI to measure true cause-and-effect relationships from observational data.
As a clinical data scientist and AI developer, I design production-grade Causal AI pipelines to evaluate treatment heterogeneity, generate synthetic counterfactuals, and inform decision-making under uncertainty.
WHAT THIS SERVICE INCLUDES:
• Causal DAG & Identifiability: Mapping domain causal assumptions (confounders, mediators, colliders) and formalizing Do-calculus identification.
• Treatment Effect Estimation: Implementing CATE, Double ML, and Causal Forests (via DoWhy & EconML) to quantify intervention impacts.
• Algorithmic Recourse & Counterfactuals: Generating realistic counterfactual explanations (DiCE framework) answering: "What minimal feature changes flip this prediction?"
• Sensitivity & Refutation: Testing model robustness against unobserved confounding.
Causal Machine Learning combines structural causal inference with modern AI to measure true cause-and-effect relationships from observational data.
As a clinical data scientist and AI developer, I design production-grade Causal AI pipelines to evaluate treatment heterogeneity, generate synthetic counterfactuals, and inform decision-making under uncertainty.
WHAT THIS SERVICE INCLUDES:
• Causal DAG & Identifiability: Mapping domain causal assumptions (confounders, mediators, colliders) and formalizing Do-calculus identification.
• Treatment Effect Estimation: Implementing CATE, Double ML, and Causal Forests (via DoWhy & EconML) to quantify intervention impacts.
• Algorithmic Recourse & Counterfactuals: Generating realistic counterfactual explanations (DiCE framework) answering: "What minimal feature changes flip this prediction?"
• Sensitivity & Refutation: Testing model robustness against unobserved confounding.
What's included
| Service Tiers |
Starter
$600
|
Standard
$1,400
|
Advanced
$2,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 2 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - | - |
About Ahmed
Clinical Biostatistician & Healthcare AI Consultant | MBBCh
Port Said, Egypt - 8:48 am local time
I consult for digital health companies, clinical research teams, and academic faculty to build, validate, and publish defensible healthcare machine learning models and biostatistical pipelines.
As a clinical candidate (MBBCh) and biostatistical consultant, I combine deep clinical literacy with rigorous machine learning methodology. I ensure your observational research, risk score calculators, and clinical AI products transition seamlessly from raw data to peer-reviewed publications or clinical deployment.
CORE CONSULTING TIERS:
Tier 1: Clinical Research Analytics & Advanced Medical Biostatistics
• Observational registry study design, propensity score matching (PSM/IPTW), and survival modeling (Cox PH, Fine-Gray competing risks).
• Handling missing data patterns via Multiple Imputation by Chained Equations (MICE).
• Drafting publication-grade Methods and Results sections compliant with STROBE, CONSORT, and PRISMA standards.
Tier 2: Clinical AI Development, External Validation & Reporting Audits
• Prognostic and diagnostic risk calculator development utilizing EHR datasets (MIMIC-IV, eICU) and clinical registries.
• Multi-cohort out-of-distribution (OOD) generalization testing, transportability auditing, and discrimination/calibration diagnostics.
• Explainable AI (SHAP / feature attribution) integration for black-box clinical models.
• Pre-submission auditing for full TRIPOD-AI and TRIPOD+AI compliance.
TECHNICAL STACK & FRAMEWORKS:
• Statistical/ML Environments: Python (Scikit-Learn, PyTorch, Pandas), R (Bioconductor, Tidyverse), SQL.
• Vocabularies & Standards: ICD-10, SNOMED CT, LOINC | TRIPOD-AI, STROBE, CONSORT.
If you require publication-ready biostatistics or rigorous external validation for your clinical ML pipeline, send a message or invite to discuss your dataset.
Steps for completing your project
After purchasing the project, send requirements so Ahmed can start the project.
Delivery time starts when Ahmed receives requirements from you.
Ahmed works on your project following the steps below.
Revisions may occur after the delivery date.
Causal Structuring & DAG Definition
Formulating causal hypotheses, building Directed Acyclic Graphs (DAGs), and testing structural identifiability
Causal ML & Counterfactual Modeling
Fitting Double ML, Causal Forests, or DiCE recourse models to estimate heterogeneous treatment effects