PhD Researchers & Doctoral Candidates
ML methods used correctly in a thesis—validated, interpreted, and explained so you can defend them against a skeptical committee.
Research Methods
Machine Learning, AI & Text-as-Data
A high accuracy score is not a finding. We bring machine learning into research the way your field expects—validated properly, explained with modern interpretability tools, and integrated with the identification and inference that make a result publishable, not just predictive.
Prediction · explainable & causal ML
NLP · text-as-data
Python · PyTorch · R
Reproducible, journal-ready
Sample output
top drivers
lower impact
Overview
Prediction that answers a research question
Machine learning entered economics, finance, and management research promising accuracy—and often delivered papers that reviewers reject for the same reasons: a model tuned on its own test set, an impressive score with no interpretation, or a causal claim resting on a method that only predicts. The tools are powerful, but they answer research questions only when they are used with the discipline the field demands.
We bring that discipline. Predictive work is validated honestly—proper cross-validation, out-of-sample testing, and regularization—and interpreted with explainable-AI tools so you can say what drives the predictions, not just how accurate they are. Where the question is causal, we use methods built for it: double machine learning for high-dimensional controls, causal forests for heterogeneous effects, hybrid econometric-ML designs that keep valid inference.
Text-as-data follows the same logic. Whether the source is central-bank communications, corporate filings, earnings calls, or social media, we build the measure—via sentiment, topic models, embeddings, or validated LLM classification—and confirm it holds before it enters a downstream model, with a pipeline that is documented and reproducible end to end.
Who We Work With
For research that reaches for modern methods
If your data is large, high-dimensional, or textual—and the result still has to satisfy a referee—this is the right desk to write to.
Finance & Economics Researchers
Prediction, text-as-data, and causal ML on market, firm, and macro data—built to publish, not just to score.
Management & IS Researchers
NLP and predictive modeling for organizational, marketing, and information-systems questions.
Research Institutes & Think Tanks
Applied ML and text analytics for evidence programs that need interpretable, reproducible pipelines.
Teams With Text or Alternative Data
Groups working with filings, news, earnings calls, or social media who need validated text measures.
Corporates & Industry R&D
Predictive analytics translated from academic rigor into decisions—with the interpretability leadership needs.
Capabilities
The full ML & text toolkit, used with discipline
Organized from prediction through explainability to text. If your project needs a method not listed here, ask—this is the core, not the boundary.
Prediction & deep learning
Modern predictive models—tree ensembles through deep and sequence architectures—validated out-of-sample.
- Random forest
- XGBoost
- LightGBM
- CatBoost
- Support vector machines
- Neural networks
- Deep learning
- LSTM
- GRU
- Transformers
Interpretation & identification
Methods that make models interpretable and, where the claim is causal, defensible under the standards of economics.
- SHAP
- Explainable AI
- Causal machine learning
- Causal forests
- Double machine learning
- Heterogeneous treatment effects
- Hybrid econometric-ML models
Turning text into measures
Building validated variables from unstructured text and non-traditional sources for downstream analysis.
- Natural language processing
- Sentiment analysis
- Topic modeling
- Text-as-data
- News analytics
- Central-bank communication analysis
- Corporate filing analysis
- Earnings-call analysis
- Social-media analytics
- LLM-based classification
- Embeddings
- Text networks
- Alternative financial data
How the Analysis Works
Six steps from data to interpretable model
A transparent, validation-first sequence—the model tuned honestly and explained, never a black box scored on its own test set.
Steps are adapted to your task: prediction vs. causal estimation, tabular vs. text, and whether interpretability or raw accuracy is the goal. We confirm the approach with you before building.
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1
Frame
Define the task, the target, and whether the goal is prediction or a causal claim—each implies a different method and standard of proof.
Inputs: task · target · predict vs. explain
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2
Prepare
Engineer features or build text measures—cleaning, tokenizing, embedding—and split data so evaluation stays honest.
Steps: feature engineering · text pipeline · train/test split
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3
Model
Train candidate models with the architecture the task requires, tuning on validation data only.
Models: ensembles · neural nets · transformers · DML
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4
Validate
Evaluate out-of-sample with the right metrics, cross-validation, and checks against overfitting and leakage.
Checks: cross-validation · hold-out · leakage · calibration
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5
Explain
Open the model with explainability tools—what drives predictions, for whom, and whether it predicts or causes.
Tools: SHAP · feature importance · partial dependence
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6
Report
Deliver performance and interpretation, figures, methodology, and a reproducible pipeline you keep.
Output: metrics · SHAP plots · methods · Python/R pipeline
Rigor by default
The checks that separate a finding from a fluke
A model that looks good on its training data proves nothing. The validation and interpretation that make an ML result credible are standard on every engagement.
Included on every project
- Honest train/validation/test separation
- Out-of-sample evaluation with task-appropriate metrics
- Checks for overfitting, leakage, and imbalance
- Explainability so results are interpretable, not opaque
- Reproducible, versioned pipeline you keep
What You Receive
Every engagement, delivered in full
Not a black-box result and a number, but a complete, documented package you can submit, defend, and reproduce.
- Clean, documented dataset and feature pipeline
- Trained models with tuning and validation records
- Out-of-sample performance with appropriate metrics
- Explainability output (SHAP, feature importance)
- Validated text measures, where applicable
- Interpretation of what drives the predictions
- Reproducible Python or R pipeline and environment
- Journal-ready methodology and results sections
- Technical responses to methodological reviewer comments, where required
Where this fits
Part of a larger arc
Machine learning is strongest when the research design ahead of it is deliberate and the reporting after it is precise—each handled with the same care.
Research Design & Planning
Identification strategy, power, and specification decided before estimation begins.
Explore methodsStatistical & Methodological Audit
An independent check of assumptions, specification, and reproducibility before submission.
Explore auditPublication & Research Support
Methods and results reporting, journal selection, and reviewer-response support.
Explore support
FAQ
Common questions
Answers to what most researchers and project leads ask before we begin a machine-learning or text-as-data engagement.
Yes, when the ML serves the research question rather than replacing it. Journals in these fields expect interpretation, not just accuracy—so we pair predictive models with explainability (SHAP, partial dependence) and, where the claim is causal, with methods like double machine learning or causal forests that have a defensible identification logic. A black-box model with a high R-squared and no interpretation rarely survives review.
Proper validation is built in: train/validation/test splits or nested cross-validation, out-of-sample evaluation, regularization, and honest reporting of performance on held-out data. We report the metrics that match the task and never tune on the test set—the practices that make results reproducible rather than lucky.
Yes—interpretability is usually the point in academic work. We use SHAP values, feature importance, partial dependence, and related explainable-AI tools to show what drives predictions, and we are explicit about the difference between a feature that predicts an outcome and one that causes it.
Yes. Text-as-data is a core capability: sentiment analysis, topic modeling, embeddings, and text networks, applied to sources such as news, central-bank communications, corporate filings, earnings calls, and social media. We can build a measure from raw text and validate it before it enters a downstream model.
Where they add rigor and can be validated. LLM-based classification and annotation can scale text coding that would otherwise be infeasible—but only with human-validated accuracy, documented prompts, and reproducible pipelines, so a reviewer can trust and replicate the measure.
Yes—hybrid econometric-ML approaches are often the strongest option. Double machine learning for high-dimensional controls, ML-generated text measures used in a regression, or causal forests for heterogeneous effects all combine predictive power with the identification and inference economics requires.
Primarily Python (scikit-learn, PyTorch, Hugging Face) and R, matched to the task. You receive documented, versioned code and a reproducible pipeline, along with a methods section written to the standards of your target journal.
Yes, and it is the best time to involve us. Whether you have the data volume, the label quality, and the signal to justify a machine-learning approach—versus a simpler model—is a question worth answering before the project is built around it.
Related Expertise
Often paired with econometrics
Reaching for machine learning?
Tell us your data and your question—we'll tell you whether ML is the right tool, which method fits, and how to make the result interpretable and publishable.