<p>Since the second policy happened at the same time, a potential issue not necessarily omitted variable bias, but that the treatment dummy is not estimating the effect of policy 1 but policy 1 + policy 2 (assuming I understand correctly they happened at the same time). I wouldn't call this omitted variable bias because the treatment dummy technically captures all effects caused by policy changes, even if they were multiple.  Second issue is that you would also have contaminated donor pool.</p>
<p>The second problem is very easy to solve just exclude the countries that also implemented the policy 2 from the donor pool.</p>
<p>The first problem is more difficult to approach. You won't be able to disentangle the effect of policy 1 and 2 within the model (assuming they occurred at the same time as you say).</p>
<p>If there shouldn't be any effect theoretically, but you still want to estimate it empirically I would try to do it in different country that implemented this  method at different time if possible. Then if the effect is not significant you could use that as additional support (besides theory) to claim there shouldn't be any effect so you are getting causal estimate of policy 1.</p>

CC BY-SA 4.0
2024-06-05T00:16:36.610Z
2024-06-05T00:16:36.610Z
1muflon1
<p>I am currently conducting a synthetic control approach and try to assess the possibility of confounding effects. In my setting, two policies had happened. One policy that (in theory) affected the outcome variable only happened in the treated unit. However, at the same time, there is another similar policy happening that did (in theory) not directly affect the outcome variable. This policy also happened in the treated unit and also in a lot of units in the donor pool. Let's say that the second policy also had an effect on the outcome variable: would this lead to omitted variable bias? Or is this the case only when either the treatment unit or some of the control units are affected by the policy? I am unsure as to what applies in this certain context, as I only find literature that states to exclude donor units that adopted similar policies or that mentions only the treatment unit adopting a similar policy at the time of the intervention. If someone could clarify this issue for me, I would appreciate it.</p>

CC BY-SA 4.0
2024-06-02T11:57:38.603Z
I think your analysis sounds good to me.
You should be able to control for this potentially confounding policy that was also introduced.

The only thing to bear in mind is that you can't identify separately an interaction effect between the two policy changes (which, if you think the policies could interact, might be of relevance).
matthewoulton
<selection-bias><synthetic-control>
Synthetic Control - Potentially confounding policy in the treated and donor units
matthew5678
# Synthetic Control - Potentially confounding policy in the treated and donor units

(Asked by: username_0 on 2024-06-01)

I am currently conducting a synthetic control approach and try to assess the possibility of confounding effects. In my setting, two policies had happened. One policy that (in theory) affected the outcome variable only happened in the treated unit. However, at the same time, there is another similar policy happening that did (in theory) not directly affect the outcome variable. This policy also happened in the treated unit and also in a lot of units in the donor pool. Let's say that the second policy also had an effect on the outcome variable: would this lead to omitted variable bias? Or is this the case only when either the treatment unit or some of the control units are affected by the policy? I am unsure as to what applies in this certain context, as I only find literature that states to exclude donor units that adopted similar policies or that mentions only the treatment unit adopting a similar policy at the time of the intervention. If someone could clarify this issue for me, I would appreciate it.

**Tags:** selection-bias, synthetic-control

> **Comments:**
> **username_1**: I think your analysis sounds good to me. You should be able to control for this potentially confounding policy that was also introduced.  The only thing to bear in mind is that you can't identify separately an interaction effect between the two policy changes (which, if you think the policies could interact, might be of relevance).  

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## 1 answers

### Answer by username_2 on 2024-06-05. Score: 0

Since the second policy happened at the same time, a potential issue not necessarily omitted variable bias, but that the treatment dummy is not estimating the effect of policy 1 but policy 1 + policy 2 (assuming I understand correctly they happened at the same time). I wouldn't call this omitted variable bias because the treatment dummy technically captures all effects caused by policy changes, even if they were multiple. Second issue is that you would also have contaminated donor pool.

The second problem is very easy to solve just exclude the countries that also implemented the policy 2 from the donor pool.

The first problem is more difficult to approach. You won't be able to disentangle the effect of policy 1 and 2 within the model (assuming they occurred at the same time as you say).

If there shouldn't be any effect theoretically, but you still want to estimate it empirically I would try to do it in different country that implemented this method at different time if possible. Then if the effect is not significant you could use that as additional support (besides theory) to claim there shouldn't be any effect so you are getting causal estimate of policy 1.


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