Support
The support of a probability distribution is the set of values to which it assigns positive probability or nonzero density.
Causal Inference for Data Science
The support of a probability distribution is the range of values that can occur with positive probability.
- How it is used
- Assesses whether treated and untreated patients have comparable propensity-score values for causal effect estimation.
- How deep it goes
- Defines support through positive probability and applies overlap to determine where average treatment effects can be estimated.
- Examples
- Uses distributions with no overlap, full overlap, partial overlap, and overlap limited to treated patients.
- What you can do after
- Evaluate causal-effect feasibility and restrict estimates to patients inside the common-support region.
Read how Causal Inference for Data Science explains support →
Fabulous Adventures in Data Structures and Algorithms
The support of a probability distribution is the set of values that can occur with positive probability.
- How it is used
- Defines which outcomes a distribution can produce and supplies the domain for sampling, probabilities, weights, and transformations.
- How deep it goes
- Explains finite and infinite discrete supports, then applies enumeration, projection, aggregation, and empty-support handling in C#.
- Examples
- Uses dice, coins, colored marbles, Random.Next(6), projected colors, and an empty distribution as concrete cases.
- What you can do after
- Evaluate/understand which outcomes a discrete distribution permits and how projections or filters change that outcome set.
Read how Fabulous Adventures in Data Structures and Algorithms explains support →
Related concepts
- uniform distributionused by · A uniform distribution assigns equal probability to each outcome in a discrete support or constant probability density across a continuous interval.
- continuous distributionpart of · A continuous distribution is a probability distribution for a variable whose possible values form a continuous range rather than a discrete set.
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