# Causal Inference in Educational Systems: A Graphical Modeling Approach

arXiv: https://arxiv.org/abs/2108.00654 · HTML: https://mlsanity.com/paper/2108.00654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Predicting Student Performance in an Educational Game Using a Hidden Markov Model](https://mlsanity.com/paper/1904.11857/) (2019)
- [tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure](https://mlsanity.com/paper/2009.04595/) (2020)

## Built on by (most cited first, 1 of 1)

- [Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces](https://mlsanity.com/paper/2603.22340/) (2026)

## Abstract (arXiv)

Educational systems have traditionally been evaluated using cross-sectional studies, namely, examining a pretest, posttest, and single intervention. Although this is a popular approach, it does not model valuable information such as confounding variables, feedback to students, and other real-world deviations of studies from ideal conditions. Moreover, learning inherently is a sequential process and should involve a sequence of interventions. In this paper, we propose various experimental and quasi-experimental designs for educational systems and quantify them using the graphical model and directed acyclic graph (DAG) language. We discuss the applications and limitations of each method in education. Furthermore, we propose to model the education system as time-varying treatments, confounders, and time-varying treatments-confounders feedback. We show that if we control for a sufficient set of confounders and use appropriate inference techniques such as the inverse probability of treatment weighting (IPTW) or g-formula, we can close the backdoor paths and derive the unbiased causal estimate of joint interventions on the outcome. Finally, we compare the g-formula and IPTW performance and discuss the pros and cons of using each method.