---
title: Limitations of Post-Hoc Causal Interpretations
url: https://www.ml-quant.com/papers/ssrn/5074003/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5074003
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5074003
featured: 2025-01-01
citations: unknown
topic: Econometrics & Forecasting
---


# Limitations of Post-Hoc Causal Interpretations

The study criticizes post hoc causal interpretations in social sciences, advocating for machine learning as a supplementary tool for causal inference, not a standalone solution.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5074003
- Identifier: SSRN 5074003
- Released: 2024-12-27
- First featured: Quant Letter No. 80 (2025-01-01): https://www.ml-quant.com/issues/2025-01-01/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Exploring the heterogeneous impacts of Indonesia’s conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests](https://www.ml-quant.com/papers/arxiv/2501.12803/): The research uses machine learning to study the effects of Indonesia's conditional cash transfer scheme on maternal health care, finding significant variations based on supply-side factors and poverty indicators.
- [Expert-aided causal discovery of ancestral graphs](https://www.ml-quant.com/papers/arxiv/2309.12032/): A novel method for causal inference uses ancestral graph sampling and expert feedback to refine causal discovery, providing uncertainty estimates and accounting for unobserved confounders.
- [The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning](https://www.ml-quant.com/papers/arxiv/2506.07421/): A machine learning study found that private tutoring in Japan can have positive socioeconomic impacts, but these are undermined by economic disparities among households.
- [Causal Machine Learning](https://www.ml-quant.com/papers/ssrn/5010187/): Causal machine learning can transform ineffective marketing campaigns into profitable ones by targeting individual treatment effects, correlating with measures of loss aversion.
- [Double/Debiased ML in Stata](https://www.ml-quant.com/papers/repec/tsj-stataj-v-24-y-2024-i-1-p-3-45/): The article presents a package named ddml for double/debiased machine learning in Stata, supporting estimators of causal parameters for five econometric models and is compatible with various supervised machine learning programs.