score matching does: learns the gradient of the log-density without normalizing

Ever wonder how we can compare apples and oranges fairly in studies?

Imagine you're comparing the effectiveness of two diets on weight loss. People with different lifestyles might choose different diets, skewing results.

Propensity score matching (PSM) helps us compare diets by pairing people with similar backgrounds, so we're not just comparing apples and oranges.

Example

Diet A is chosen by 70% of people who exercise regularly, while Diet B is chosen by 30% of people who don't. PSM pairs someone who exercises with someone who doesn't, making the comparison fairer.

Remember this

PSM allows us to match individuals with similar characteristics, isolating the diet's effect on weight loss.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

Related concepts

denoising score matching does: learns to denoise, which equals learning the score

Can you clean up a noisy picture?

to standardize: when you need zero mean and unit variance for gradient-based optimization

Why do we need to make data uniform before training a model?

Boosting (machine learning)

Boosting reduces bias in ML models

batch size affects generalization: larger batches find sharper minima

Larger batch sizes lead to sharper minima, enhancing generalization by providing more accurate gradient estimates

soft targets carry more information than hard labels: they encode class similarities

Why do some learning methods need to explore more than others?

Proximal gradient methods for learning

Why can't we always find the best path in a maze?

Swipe through 100 ML concepts daily

Open Pocket Polymath