"Estimating Treatment Effect with Partial Results"

Estimating Treatment Effect When Some Subjects Receive Partial Treatment Effect

When conducting A/B testing on web or mobile, it is possible that some subjects may not receive the full treatment effect due to various reasons such as technical issues or user behavior. In such cases, it is important to estimate the treatment effect accurately to make informed decisions.

One approach to estimating treatment effect when some subjects receive partial treatment effect is to use statistical methods such as regression analysis or propensity score matching. These methods can help adjust for the differences between the treatment and control groups and provide a more accurate estimate of the treatment effect.

Another approach is to conduct sensitivity analysis to assess the impact of partial treatment effect on the overall results. This involves varying the assumptions and parameters used in the analysis to see how sensitive the results are to these changes.

Overall, it is important to carefully consider the impact of partial treatment effect when conducting A/B testing and to use appropriate methods to estimate the treatment effect accurately.