Search Results for author:
Found 9 papers, 3 papers with code
SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling
Causal inference is a crucial goal of science, enabling researchers to arrive at meaningful conclusions regarding the predictions of hypothetical interventions using observational data.
A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists
We present a new process which begins with the incorporation of techniques from the confluence of causal discovery and machine learning for the development, validation, and transparent formal specification of theories.
Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables
Furthermore, researchers concerned with imposing overly restrictive functional form (e. g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables which are associated with an outcome of interest.
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Unfortunately, they are unlikely to be sufficiently flexible to be able to adequately model real-world phenomena, and may yield biased estimates.
Shadow-Mapping for Unsupervised Neural Causal Discovery
An important goal across most scientific fields is the discovery of causal structures underling a set of observations.
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts
Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM).
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Causal reasoning is a crucial part of science and human intelligence.
NestedVAE: Isolating Common Factors via Weak Supervision
Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation of its paired image.
Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement
However, there is some debate about how to encourage disentanglement with VAEs and evidence indicates that existing implementations of VAEs do not achieve disentanglement consistently.
