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Found 21 papers, 7 papers with code
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph.
Entropic Causal Inference
We show that the problem of finding the exogenous variable with minimum entropy is equivalent to the problem of finding minimum joint entropy given $n$ marginal distributions, also known as minimum entropy coupling problem.
Active Structure Learning of Causal DAGs via Directed Clique Tree
Most existing works focus on worst-case or average-case lower bounds for the number of interventions required to orient a DAG.
Active Structure Learning of Causal DAGs via Directed Clique Trees
Most existing works focus on \textit{worst-case} or \textit{average-case} lower bounds for the number of interventions required to orient a DAG.
Learning Causal Graphs with Small Interventions
We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case.
Sparse Quadratic Logistic Regression in Sub-quadratic Time
We consider support recovery in the quadratic logistic regression setting - where the target depends on both p linear terms $x_i$ and up to $p^2$ quadratic terms $x_i x_j$.
Cost-Optimal Learning of Causal Graphs
We consider the problem of learning a causal graph over a set of variables with interventions.
Entropic Causality and Greedy Minimum Entropy Coupling
This framework requires the solution of a minimum entropy coupling problem: Given marginal distributions of m discrete random variables, each on n states, find the joint distribution with minimum entropy, that respects the given marginals.
Contextual Bandits with Latent Confounders: An NMF Approach
Our algorithm achieves a regret of $\mathcal{O}\left(L\mathrm{poly}(m, \log K) \log T \right)$ at time $T$, as compared to $\mathcal{O}(LK\log T)$ for conventional contextual bandits, assuming a constant gap between the best arm and the rest for each context.
Sparse Polynomial Learning and Graph Sketching
We give an algorithm for exactly reconstructing f given random examples from the uniform distribution on $\{-1, 1\}^n$ that runs in time polynomial in $n$ and $2s$ and succeeds if the function satisfies the unique sign property: there is one output value which corresponds to a unique set of values of the participating parities.
Applications of Common Entropy for Causal Inference
We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent.
Experimental Design for Cost-Aware Learning of Causal Graphs
We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph.
Experimental Design for Learning Causal Graphs with Latent Variables
Next, we propose an algorithm that uses only O(d^2 log n) interventions that can learn the latents between both non-adjacent and adjacent variables.
Sample Efficient Active Learning of Causal Trees
We consider the problem of experimental design for learning causal graphs that have a tree structure.
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
We introduce a novel notion of interventional equivalence class of causal graphs with latent variables based on these invariances, which associates each graphical structure with a set of interventional distributions that respect the do-calculus rules.
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
One fundamental problem in the empirical sciences is of reconstructing the causal structure that underlies a phenomenon of interest through observation and experimentation.
Entropic Causal Inference: Identifiability and Finite Sample Results
This unobserved randomness is measured by the entropy of the exogenous variable in the underlying structural causal model, which governs the causal relation between the observed variables.
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when the causal variables are unobserved and the observations are non-linear mixtures of these latent variables, such as pixels in images.
Approximate Causal Effect Identification under Weak Confounding
We show that our bounds are consistent in the sense that as the entropy of unobserved confounders goes to zero, the gap between the upper and lower bound vanishes.
Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
To address this, we propose a sequential training algorithm that, given the causal structure and a pre-trained conditional generative model, can train a deep causal generative model, which utilizes the pre-trained model and can provably sample from identifiable interventional and counterfactual distributions.
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
As an application of our algorithm, we evaluate two large conditional generative models that are pre-trained on the CelebA dataset by analyzing the strength of spurious correlations and the level of disentanglement they achieve.
