# Statistical modeling: the three cultures

arXiv: https://arxiv.org/abs/2012.04570 · HTML: https://mlsanity.com/paper/2012.04570/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 18 of 18)

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- [Deep Learning With DAGs](https://mlsanity.com/paper/2401.06864/) (2024)
- [A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty](https://mlsanity.com/paper/2406.02584/) (2024)
- [Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa](https://mlsanity.com/paper/2206.06410/) (2022)
- [Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs](https://mlsanity.com/paper/2411.02134/) (2024)
- [Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows](https://mlsanity.com/paper/2202.09391/) (2022)
- [Machine Learning and Theory Ladenness -- A Phenomenological Account](https://mlsanity.com/paper/2409.11277/) (2024)
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- [Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability](https://mlsanity.com/paper/2310.13240/) (2023)
- [Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis](https://mlsanity.com/paper/2508.01341/) (2025)
- [Counterfactually Fair Regression with Double Machine Learning](https://mlsanity.com/paper/2303.11529/) (2023)
- [CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images](https://mlsanity.com/paper/2310.00233/) (2023)
- [Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach](https://mlsanity.com/paper/2411.02855/) (2024)
- [Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?](https://mlsanity.com/paper/2508.01109/) (2025)
- [Detecting and Mitigating Treatment Leakage in Text-Based Causal Inference: Distillation and Sensitivity Analysis](https://mlsanity.com/paper/2601.02400/) (2025)
- [Beyond Interaction Effects: Two Logics for Studying Population Inequalities](https://mlsanity.com/paper/2601.04223/) (2025)

## Abstract (arXiv)

Two decades ago, Leo Breiman identified two cultures for statistical modeling. The data modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or several quantities of interest. The algorithmic modeling culture (AMC) refers to practices defining a machine-learning (ML) procedure that generates accurate predictions about an event of interest. Breiman argued that statisticians should give more attention to AMC than to DMC, because of the strengths of ML in adapting to data. While twenty years later, DMC has lost some of its dominant role in statistics because of the data-science revolution, we observe that this culture is still the leading practice in the natural and social sciences. DMC is the modus operandi because of the influence of the established scientific method, called the hypothetico-deductive scientific method. Despite the incompatibilities of AMC with this scientific method, among some research groups, AMC and DMC cultures mix intensely. We argue that this mixing has formed a fertile spawning pool for a mutated culture that we called the hybrid modeling culture (HMC) where prediction and inference have fused into new procedures where they reinforce one another. This article identifies key characteristics of HMC, thereby facilitating the scientific endeavor and fueling the evolution of statistical cultures towards better practices. By better, we mean increasingly reliable, valid, and efficient statistical practices in analyzing causal relationships. In combining inference and prediction, the result of HMC is that the distinction between prediction and inference, taken to its limit, melts away. We qualify our melting-away argument by describing three HMC practices, where each practice captures an aspect of the scientific cycle, namely, ML for causal inference, ML for data acquisition, and ML for theory prediction.