# Building Object-based Causal Programs for Human-like Generalization

arXiv: https://arxiv.org/abs/2111.12560 · HTML: https://mlsanity.com/paper/2111.12560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 58 references that are in the map.
- First posted on arXiv 20 Nov 2021.

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

- [People infer recursive visual concepts from just a few examples](https://mlsanity.com/paper/1904.08034/) (2019)
- [A General Algorithm for Deciding Transportability of Experimental Results](https://mlsanity.com/paper/1312.7485/) (2013)
- [Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning](https://mlsanity.com/paper/1609.04212/) (2016)

## Abstract (arXiv)

We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational modeling framework that can synthesize human-like generalization patterns in our task setting, and sheds light on how people may navigate the compositional space of possible causal functions and categories efficiently. Our modeling framework combines a causal function generator that makes use of agent and recipient objects' features and relations, and a Bayesian non-parametric inference process to govern the degree of similarity-based generalization. Our model has a natural "resource-rational" variant that outperforms a naive Bayesian account in describing participants, in particular reproducing a generalization-order effect and causal asymmetry observed in our behavioral experiments. We argue that this modeling framework provides a computationally plausible mechanism for real world causal generalization.