Adjusting for Confounding in Unsupervised Latent Representations of Images
Biological imaging data are often partially confounded or contain unwanted variability. Examples of such phenomena include variable lighting across microscopy image captures, stain intensity variation in histological slides, and batch effects for high throughput drug screening assays. Therefore, to develop "fair" models which generalise well to unseen examples, it is crucial to learn data representations that are insensitive to nuisance factors of variation. In this paper, we present a strategy based on adversarial training, capable of learning unsupervised representations invariant to confounders. As an empirical validation of our method, we use deep convolutional autoencoders to learn unbiased cellular representations from microscopy imaging.
Code (1)
Similar Papers 제목 키워드 기반
Causal Inference from Text: Unveiling Interactions between Variables
Adjusting for latent covariates is crucial for estimating causal effects from observational textual data. Most existing methods only account for confounding covariates that affect both treatment and outcome, potentially …
Causal InferenceSelection biasDisentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observational data, for example, confounding b…
Causal InferenceRepresentation LearningPrediction under Latent Subgroup Shifts with High-Dimensional Observations
We introduce a new approach to prediction in graphical models with latent-shift adaptation, i.e., where source and target environments differ in the distribution of an unobserved confounding latent variable. Previous wor…
Adjustment for Confounding using Pre-Trained Representations
There is growing interest in extending average treatment effect (ATE) estimation to incorporate non-tabular data, such as images and text, which may act as sources of confounding. Neglecting these effects risks biased re…
parameter estimationTransfer LearningSanitized Clustering against Confounding Bias
Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding factor that disturbs the cluster analysi…
Clustering