arXiv research

Review of methods enabling causal predictions under hypothetical interventions.

New approach to counterfactual reasoning avoids demographic interventions.

Bayesian method estimates intervention effects in non-linear data.

Estimates outcomes under hypothetical scenarios using a flexible framework.

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) p…

Study uses ML and causal analysis to predict student performance factors.

Algorithm simulates counterfactuals for fairness analysis.

Unified framework for estimating indirect effects in observational studies with unmeasured confounding.

New approach uses 'forward-looking' counterfactuals for treatment choice.

New method uncovers small but significant local activities in time-series data.

A new concept of causality for abstract phenomena.

These are the notes for the talk "Hodge numbers of a hypothetical complex structure on $S^6$" given by the author at the MAM1 "(Non)-existence of complex structures on $S^6$" held in Marburg in March 2017. They are based on [A. Gray, A property of a hypothetical complex structure on the six sphere, Boll. Un. Mat. Ital.…

Develops algorithm to reduce real-world inequality.

This article is a survey about or introduction to certain aspects of the complex geometry of a hypothetical complex structure on the six-sphere. We discuss a result of Peternell--Campana--Demailly on the algebraic dimension of a hypothetical complex six-sphere and give some examples. We also give an overview over an ap…

Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…

MSCT predicts post-crash traffic speed using causal inference.

D2D converts CLDs into SDMs to explore leverage points under uncertainty.

SLEM uses machine learning to improve causal inference from observational data.

Developed a flexible Bayesian g-formula for causal survival analysis with time-dependent confounding.

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns dis…

New approach to counterfactual reasoning in AI and psychology.

DiffLOB models future market conditions for better decision-making.

We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and …

The world of cryptocurrency is not transparent enough though it was established for innate transparent tracking of capital flows. The most contributing factor is the violation of securities laws and scam in Initial Coin Offering (ICO) which is used to raise capital through crowdfunding. There is a lack of proper regula…

Estimates joint causal effects using single-variable interventions on nonlinear models.

This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.

This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.

Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…

Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change…

Paper proposes scalable algorithm to estimate intervention targets in linear models.

New method disentangles mixed interventional and observational data in SEMs.

The paper presents a method to estimate joint interventional distributions from marginal interventional data.

Bayesian method for causal discovery from unknown general interventions.

Interventional data helps identify latent factors without distributional assumptions.

IntDC framework uncovers causal relationships from non-interventional data.

Algorithm detects causal change points quickly with adaptive interventions.

Method learns causal effects from multiple interventions in presence of unobserved confounders.

New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.

We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…

GIT uses gradient estimators to target interventions for causal discovery.

Paper relaxes faithfulness assumption for causal discovery using interventions.

Estimating arbitrary quantities of interest (QoIs) that are non-linear operators of complex, expensive-to-evaluate, black-box functions is a challenging problem due to missing domain knowledge and finite budgets. Bayesian optimal design of experiments (BODE) is a family of methods that identify an optimal design of exp…

FMI uses matching to mimic interventions for causal feature learning.

The paper explores how macroeconomic variables' correlation structure changes over time and under different scenarios.

BaCaDI discovers causal structures from unknown interventions.

Graph-coupled causal Bayesian optimization transfers information across related interventions.

The paper tackles matching a desired mean in causal systems through shift interventions.

Intervention loss stabilizes GAN training and reduces mode collapse.