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Found 26 papers, 10 papers with code
Machine Explanations and Human Understanding
Our key result is that without assumptions about task-specific intuitions, explanations may potentially improve human understanding of model decision boundary, but they cannot improve human understanding of task decision boundary or model error.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment or policy making.
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Therefore, simple plausibility judgments on the maximum explanatory power of omitted variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias.
Matching Learned Causal Effects of Neural Networks with Domain Priors
A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output.
Sayer: Using Implicit Feedback to Optimize System Policies
We develop a methodology, called Sayer, that leverages implicit feedback to evaluate and train new system policies.
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Through extensive evaluation on a synthetic dataset and image datasets like MNIST, Fashion-MNIST, and Chest X-rays, we show that a lower OOD generalization gap does not imply better robustness to MI attacks.
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed.
Noise2Fast: Fast Self-Supervised Single Image Blind Denoising
Recent approaches have allowed for the denoising of single noisy images without access to any training data aside from that very image.
Causally Constrained Data Synthesis for Private Data Release
However for real-world applications, the privacy of data is critical.
On Cofibrations of Permutative categories
In this note we introduce a notion of free cofibrations of permutative categories.
Category Theory Algebraic Topology
Technology Readiness Levels for Machine Learning Systems
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
For example, in multi-stage settings where decisions are made in multiple screening rounds, we use our framework to derive the minimal distributions required to design a fair algorithm.
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm.
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.
DoWhy: An End-to-End Library for Causal Inference
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals
We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI), that generates counterfactuals in accordance with the causal relationships between attributes of an image.
Domain Generalization using Causal Matching
In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label.
Ranked #1 on
Domain Generalization
on Rotated Fashion-MNIST
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi
The primary obstacle to developing technologies for low-resource languages is the lack of usable data.
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world.
Alleviating Privacy Attacks via Causal Learning
Such privacy risks are exacerbated when a model's predictions are used on an unseen data distribution.
Quantifying Infra-Marginality and Its Trade-off with Group Fairness
To quantify this bias, we propose a general notion of $\eta$-infra-marginality that can be used to evaluate the extent of this bias.
Quantifying Error in the Presence of Confounders for Causal Inference
We show that many popular methods, including back-door methods can be considered as weighting or representation learning algorithms, and provide general error bounds for their causal estimates.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
Split-door criterion: Identification of causal effects through auxiliary outcomes
We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available.
Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures
The proposed algorithms use a best first search technique and report the solutions using an implicit representation ordered by cost.
