21 research outputs found
On the Tractability of Neural Causal Inference
Roth (1996) proved that any form of marginal inference with probabilistic
graphical models (e.g. Bayesian Networks) will at least be NP-hard. Introduced
and extensively investigated in the past decade, the neural probabilistic
circuits known as sum-product network (SPN) offers linear time complexity. On
another note, research around neural causal models (NCM) recently gained
traction, demanding a tighter integration of causality for machine learning. To
this end, we present a theoretical investigation of if, when, how and under
what cost tractability occurs for different NCM. We prove that SPN-based causal
inference is generally tractable, opposed to standard MLP-based NCM. We further
introduce a new tractable NCM-class that is efficient in inference and fully
expressive in terms of Pearl's Causal Hierarchy. Our comparative empirical
illustration on simulations and standard benchmarks validates our theoretical
proofs.Comment: Main paper: 8 pages, References: 2 pages, Appendix: 5 pages. Figures:
5 main, 2 appendi
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
Some argue scale is all what is needed to achieve AI, covering even causal
models. We make it clear that large language models (LLMs) cannot be causal and
give reason onto why sometimes we might feel otherwise. To this end, we define
and exemplify a new subgroup of Structural Causal Model (SCM) that we call meta
SCM which encode causal facts about other SCM within their variables. We
conjecture that in the cases where LLM succeed in doing causal inference,
underlying was a respective meta SCM that exposed correlations between causal
facts in natural language on whose data the LLM was ultimately trained. If our
hypothesis holds true, then this would imply that LLMs are like parrots in that
they simply recite the causal knowledge embedded in the data. Our empirical
analysis provides favoring evidence that current LLMs are even weak `causal
parrots.'Comment: Published in Transactions in Machine Learning Research (TMLR)
(08/2023). Main paper: 17 pages, References: 3 pages, Appendix: 7 pages.
Figures: 5 main, 3 appendix. Tables: 3 mai
Differentiable Meta logical Programming
Deep learning uses an increasing amount of computation and data to solve very
specific problems. By stark contrast, human minds solve a wide range of
problems using a fixed amount of computation and limited experience. One
ability that seems crucial to this kind of general intelligence is
meta-reasoning, i.e., our ability to reason about reasoning. To make deep
learning do more from less, we propose the differentiable logical meta
interpreter (DLMI). The key idea is to realize a meta-interpreter using
differentiable forward-chaining reasoning in first-order logic. This directly
allows DLMI to reason and even learn about its own operations. This is
different from performing object-level deep reasoning and learning, which
refers in some way to entities external to the system. In contrast, DLMI is
able to reflect or introspect, i.e., to shift from meta-reasoning to
object-level reasoning and vice versa. Among many other experimental
evaluations, we illustrate this behavior using the novel task of "repairing
Kandinsky patterns," i.e., how to edit the objects in an image so that it
agrees with a given logical concept
Pearl Causal Hierarchy on Image Data: Intricacies & Challenges
Many researchers have voiced their support towards Pearl's counterfactual
theory of causation as a stepping stone for AI/ML research's ultimate goal of
intelligent systems. As in any other growing subfield, patience seems to be a
virtue since significant progress on integrating notions from both fields takes
time, yet, major challenges such as the lack of ground truth benchmarks or a
unified perspective on classical problems such as computer vision seem to
hinder the momentum of the research movement. This present work exemplifies how
the Pearl Causal Hierarchy (PCH) can be understood on image data by providing
insights on several intricacies but also challenges that naturally arise when
applying key concepts from Pearlian causality to the study of image data.Comment: Main paper: 9 pages, References: 2 pages. Main paper: 7 figure