<p>I would not do that. Your problem is that the treatment assignment is not independent of your covariates. As such, it is unlikely that the potential outcomes <span class="math-container">$Y_0$</span> (my outcome in the world &quot;I take the treatment&quot;) and <span class="math-container">$Y_1$</span> (my outcome in the world &quot;I do not take the treatment&quot;) are independent of treatment assignment <span class="math-container">$D\in\{0,1\}$</span>.</p>
<p>What you can do is use estimation techniques that would allow you to validate the conditional independence assumption (i.e., given covariates <span class="math-container">$X$</span>, treatment assignment <span class="math-container">$D$</span> is independent of potential outcomes <span class="math-container">$Y_0,Y_1$</span>). Bear in mind that you can never claim you've reached independence as there will always be covariates you don't measure.</p>
<p>These techniques include: running an OLS with controls, matching treatment with control units with similar propensity scores, weighting with the propensity score etc.</p>
<p>Good luck!</p>

CC BY-SA 4.0
2023-07-13T11:16:27.103Z
2023-07-13T11:16:27.103Z
ju_pi_car
<p>in an RCT, I have 3 different treatment groups and one control group. The size of the control group is around 1000 while the size of other groups are just above 300. </p>

<p>To test balance I used ANOVA and Welch test, which show that several variables are unbalanced. Can I draw a smaller random sample (400) from the control group so that the sizes of all groups are relatively equal?</p>

<p>Actually after doing that only one variable is unbalanced. So would it cause any problems if I want to draw a smaller sample just for more balance? Thanks!</p>

CC BY-SA 4.0
2019-03-25T06:26:02.103Z
If you haven't already, recommend also posting this question on the statistics site: http://stats.stackexchange.com/.
AlexK
CC BY-SA 4.0
2019-03-25T06:32:57.327Z
There is already a similar question there with an answer that discourages this practice: https://stats.stackexchange.com/q/71933/241093
AlexK
<econometrics><development-economics><randomized-experiment>
Can I draw a smaller sample from one treatment group?
Aris
# Can I draw a smaller sample from one treatment group?

(Asked by: username_0 on 2019-03-23)

in an RCT, I have 3 different treatment groups and one control group. The size of the control group is around 1000 while the size of other groups are just above 300.

To test balance I used ANOVA and Welch test, which show that several variables are unbalanced. Can I draw a smaller random sample (400) from the control group so that the sizes of all groups are relatively equal?

Actually after doing that only one variable is unbalanced. So would it cause any problems if I want to draw a smaller sample just for more balance? Thanks!

**Tags:** econometrics, development-economics, randomized-experiment

> **Comments:**
> **username_1**: If you haven't already, recommend also posting this question on the statistics site: http://stats.stackexchange.com/.  
> **username_1**: There is already a similar question there with an answer that discourages this practice: https://stats.stackexchange.com/q/71933/241093  

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## 1 answers

### Answer by username_2 on 2023-07-13. Score: 0

I would not do that. Your problem is that the treatment assignment is not independent of your covariates. As such, it is unlikely that the potential outcomes $Y\_0$ (my outcome in the world "I take the treatment") and $Y\_1$ (my outcome in the world "I do not take the treatment") are independent of treatment assignment $D\in\{0,1\}$.

What you can do is use estimation techniques that would allow you to validate the conditional independence assumption (i.e., given covariates $X$, treatment assignment $D$ is independent of potential outcomes $Y\_0,Y\_1$). Bear in mind that you can never claim you've reached independence as there will always be covariates you don't measure.

These techniques include: running an OLS with controls, matching treatment with control units with similar propensity scores, weighting with the propensity score etc.

Good luck!


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