Advances in Causal Inference With Continuous Exposures — NSF Award to University of Massachusetts Amherst (MA, $177,828)
Determining cause and effect is one of the fundamental goals of scientific inquiry. For instance, does a vaccine reduce risk of disease? Is a chemical such as lead or ammonia in drinking water harmful to human health? Causal inference is the area of statistical research concerned with developing methods for using data
| Award title | Advances in Causal Inference With Continuous Exposures |
|---|---|
| Award ID | 2113171 |
| Awardee | University of Massachusetts Amherst |
| City | AMHERST |
| State | MA |
| Amount obligated | $177,828 |
| Principal investigator | Theodore Westling |
| Program | STATISTICS |
| Start date | 07/15/2021 |
| Abstract | Determining cause and effect is one of the fundamental goals of scientific inquiry. For instance, does a vaccine reduce risk of disease? Is a chemical such as lead or ammonia in drinking water harmful to human health? Causal inference is the area of statistical research concerned with developing methods for using data to answer such questions. The majority of causal inference research has focused on binary exposures; that is, exposures that can only take two values, such as treatment and control |
| Source | NSF Awards |
$799/mo
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