Questions tagged [causalimpact]
CausalImpact R package for estimating the effect of an intervention on a time series.
76
questions
4
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1answer
23 views
causal impact estimation
say i have following causal model:
outcome variable: Y (e.g. sales)
treatment variable: T (e.g. price)
covariate variable: x2 (e.g. traffic)
unobserved variables: U (unobserved)
causal relation:
...
0
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0answers
10 views
how to run causalImpact on a time series with multiple interventions?
In CausalImpact package, one defines pre-period and post-period for a single intervention. However, in the scenarios where there are multiple interventions at irregular time intervals, is there a way ...
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0answers
23 views
What is a collider
The term "collider" is usually taken to mean a variable in a causal relationship that opens up spurious correlation -- usually between the outcome and the treatment variable (and hence would ...
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0answers
15 views
Modeling year-level seasonality in R-package CausalImpact for bayesian structural time-series
I have a time-series history of 2 years with spikes occcuring once a year (christmas time) and am trying to model it with a seasonal component tat catches this yearly spike.
However, no matter if I ...
0
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0answers
29 views
Why diff-in-diff over regression with control variable for measuring treatment intervention in time series?
Let's suppose we have a time series data set, with an intervention period for the treatment where the mean of the independent variable increases by 10. The goal is to measure the effect of the ...
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0answers
12 views
Isolating and Quantifying Price Change effects in Products
Let say, I changed prices in my products or changed rebates for quantity. Then, after a while I want to analyse what effect this particular measure caused on my revenues. Of course there are some ...
0
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1answer
36 views
CausalImpact plot upper and lower bounds
Below is a screenshot of causal impact result.
Upper and lower bounds of point wise results plot hovers around 10k and -10k. In summery statistics, sd shows 1879 with 95 CI of -4674 and 2736. Is the ...
0
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1answer
54 views
CausalImpact estimates a statistically significant impact for multiple intervention-free validation periods
While trying to validate the estimated impact by CausalImpact package I ran backtests on several dummy pre and post time-periods prior to the actual intervention but ended up with statistically ...
1
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1answer
70 views
Causal impact R - Is it possible to model multiple the pre and post periods? Indivudally for participants
I would like to use the causal impact algorithm, however, not in the context of marketing, but in medicine. The problem is that the intervention does not take place at the same time, but on an ...
2
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1answer
240 views
Staggered Diff-in-Diff: multiple treatments equation
I wonder if you can help me to figure out how to rewrite basic difference-in-difference equation (pictured) so that it takes into account the fact that treatment has occurred at different times for ...
1
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0answers
51 views
Combine Different Metrics to CausalImpact
I am currently trying to understand the Google package of CausalImpact, to be more concrete the python implementation.
My current state is that I have for example the sales of a car on different ...
0
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1answer
34 views
How to test the stability assumption when using the CausalImpact package in R?
In the case of CausalImpact it is assumed that the relationship between covariates and treated time series, as established during the pre-period, remains stable throughout the post-period.
Why is it ...
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0answers
67 views
How to use CausalImpact in R to construct an interrupted time series model with >2 periods of interest?
I would like to conduct an interrupted time series analysis on data with three time periods:
Pre intervention
During intervention
Post intervention
My outcome variable is a continuous biological ...
0
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0answers
47 views
Confidence Interval not showing on causalImpact Plot
I am a beginner on R studio. I am using CausalImpact Package to estimate the effect of an intervention. However, my causalImpact plot is not showing confidence intervals of the prediction. Besides, ...
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0answers
82 views
Logistic Regression Coefficient Intrepretation - Impact On Churn
I am using logistic to see the relationship between churn and minute seen.
I get something like this
...
-1
votes
1answer
102 views
Using CausalImpact to estimate uplift attributed to a promotion for logged-in users
I'm working for an online travel agency, and we've been exploring the use of the causalImpact algorithm for the following problem:
Hotel owners that sell room-...
0
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0answers
96 views
R Causal Impact package implementation
I'm trying to apply the CausalImpact R package and have few questions I'd like to clarify before proceeding:
Is the ...
0
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1answer
111 views
These summary statements seem contradictory, or have I misunderstood something?
So here's the output summary I got when running an analysis.
This means that, although it may look as though the intervention has exerted a negative effect on the response variable when considering ...
0
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1answer
169 views
Causal Impact: how to show all data points in predicted series? [closed]
This might be a very basic code question, but thank you for your help. I have used the Causal Impact package to run a regression on a data set with 144 monthly observations, cutting at point 88. I ...
2
votes
1answer
219 views
Estimate the time series like an event was never happened
I have data from a website where a specific advertising campaign happened a couple of years ago. What I want to do is to estimate how the signups on that website would have been without that big ...
0
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1answer
88 views
Matching subjects with themselves when evaluating short term outcomes
I am considering a simple causal inference scenario; Let's say we want to examine the effect of paracetamol (treatment) on curing headache (outcome).
When performing matching, is it okay to match ...
1
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0answers
49 views
Ok to use PSM to create treatment groups and then plug into CausalImpact? [closed]
Is it ok to use propensity score matching to create treatment and control groups and then plug these two time series into CausalImpact to estimate your treatment effect? I might want to do this, for ...
1
vote
1answer
256 views
CausalImpact Conflicting Results - Posterior prob. v Confidence Interval
I'm getting conflicting results from a Causal Impact analysis I'm running. The 95% C.I. indicates that 0 is included and thus the impact is not significant; however the Posterior Probability is <....
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0answers
203 views
How should I specify seasonality in CausalImpact? [closed]
I am working with the Python implementation of Google's CausalImpact package. My data is at daily frequency (365 observations per year); however, to inspect the effect of intervention, my pre-period ...
0
votes
1answer
95 views
Using CausalImpact in R, observed data affected by event that could not affect priors
I am just starting out using the Causal Impact package in R. I am looking at observed data where I know there will be periods of extra traffic due to outside forces. However I cant find any unbiased ...
3
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0answers
146 views
How to calculate causal effects with repeated exogenous shocks over a time series
A rather frequent problem in causal inference is that we come across various shocks over time and try to measure their impact.
In the case of a single shock we can use bayesian methods to predict how ...
1
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0answers
128 views
Default CausalImpact Package
I am trying to gain an understanding of the CausalImpact package and the BSTS approach. Consider the following model:
$y_t = \mu_t + z_t + \epsilon_t$, where $\epsilon_t \sim N(0,s^2)$, and
$\mu_{t+1} ...
0
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1answer
191 views
How is the posterior tail-area probability calculated?
I am currently using the CausalImpact package for some research and in this context I need to know and be able to explain, how the posterior tail-area probability is calculated in order to reproduce ...
1
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0answers
52 views
inclusion of static variables as covariates [closed]
Is there a clear way to include static variables e.g. elevation as covariates in the CausalImpact pacakge. Any guidance would be greatly appreciated.
Anand
0
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1answer
113 views
Incorporating Fixed Effects with Causal Impact
I have observations on a dependent variable of interest, $Y_{ijt}$, where $t \in [1,2,...T]$ denotes the time, $i$ denotes a particular brand and $j$ a specific product item. At $t=k$, there was an ...
3
votes
1answer
339 views
Synthetic control and unobserved confounders
The synthetic control (cohort) method is a very promising approach to causal inference that has been used in a number of interesting studies. It's particularly useful in situations where data are only ...
0
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1answer
91 views
Pre.intervention and post.intervention should be contiguous in CausalImpact?
I am running a CausalImpact analysis on a time series and my pre.period goes from 01.01.15 to 30.03.15. I want my post period to be from 15.04.15 to 17.04.15.
Is it ok if I create a time series that ...
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0answers
36 views
Assesing impact for rolling enrollment data
I'm running an experiment where some of the users are shown a new widget when they become eligible (some conditions that are unrelated to the experiment itself). I have a control counterfactual group ...
1
vote
1answer
105 views
Is it possible to predict the pre-intervention period rather than the post-period using Google's CausalImpact function?
I want to use to Google's causal impact function to impute the effect of an intervetion. However, my data is structured as: pre-period=1991-1995, intervetion occurs, post-period=1996-2017. To clarify, ...
3
votes
2answers
814 views
How to choose Control groups for Causal Impact algorithm?
I'm running an experiment and want to use the Causal Impact function to assess how well it performs.
I have 10 different cities. I'm looking to find out what is the best method for choosing which ...
0
votes
1answer
210 views
How to use a custom model in CausalImpact where preperiod.start is not right after preperiod.end?
I want to train on a period that isn't immediately preceding the prediction period. You can do this using the default causal model but I'm not sure how with a custom model.
Straight from the ...
0
votes
2answers
802 views
Interpreting Mediation Output when ACME is stat. sig but ADE and Total are not
The mediation package in R returns results in which:
The Average Causal Mediated Effect (ACME) (the effect of the mediator alone) is positive and statistically significant
Average Direct Effect (ADE) ...
3
votes
0answers
499 views
Pro and cons between Bayesian structural time series (BSTS) vs difference-in-differences?
Google's paper markets BSTS's benefits over DID such that
"In contrast to classical difference-in-differences schemes,
state-space models make it possible to (i) infer the temporal
evolution of ...
1
vote
1answer
212 views
Explaining the results of the CausalImpact package where effect is not statistically significant
I've been using the CausalImpact package to compare patent renewal rates across different classes of patents (to determine whether subject matter decisions have particular impacts on the rise or fall ...
2
votes
2answers
2k views
How to study the causal impact on multiple time series of interest against multiple baselines
My data consist of time series of Wikipedia hits for football players. I want to use the CausalImpact package to explore the effect of the World Cup on their ...
1
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0answers
310 views
Causal Impact: Continuous Pre-Intervention to Post-Intervention Time Periods?
Within the context of using R package CausalImpact (Brodersen et. al, 2015), is it valid to use time Periods that are noncontinuous for the Pre-Intervention and Post Intervention time periods?
For ...
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vote
2answers
161 views
CausalImpact: Valid for Single Market [closed]
Is it possible to use the R package CausalImpact (Brodersen et. al, 2015) to estimate the incremental lift of running a local TV ad campaign in Iowa City,IA? We are also running national TV ads across ...
-1
votes
1answer
253 views
Generalizing “Causal Impact” synthetic controls, to multiple outcomes
Does anybody know a way to generalize the use of the Causal Impact google R package to multiple outcome time series?
Say I ran a time series experiment and was able to set up multiple test outcome ...
2
votes
1answer
758 views
Scaling control time series with CausalImpact
I'm doing Causal Impact analytics with this python package. Since my control time series have a much larger scale (100-10000 times larger) than my modeled variable, at some point I tried to scale the ...
3
votes
1answer
602 views
Reduce credible intervals in Causal Impact model
I'm hitting an issue with a causal impact model that I'm building.
I'm trying to create a counter factual for daily sales at one store (nseasons = 7). I've included sales for 5 other stores nearby. ...
0
votes
1answer
125 views
How can you quantify the effect of a campaign in CausalImpact?
I have been working with the CausalImpact R-package to check whether new campaigns or features had a significant impact on a time-series.
I would like to know if it is possible to quantify this ...
1
vote
1answer
266 views
How to reason about causal effect in time series when the treatment group reacts even before treatment?
Context:
I'm running into a strange phenomenon about treatment effect and causality that I'll try to recreate here. Let's say I'm doing an observational study: Do people spend more time online as a ...
3
votes
1answer
891 views
Difference between using propensity score matching and CausalImpact for causal inference?
I'm investigating causal effect in some financial data, and I'm using two different approaches: propensity score matching with stratification and the CausalImpact package for Bayesian structural time ...
0
votes
1answer
244 views
BSTS employing purposedly spurious regressors for causal impact analysis
I'm employing Google Correlate time series to evaluate the causal impact of an intervention on a variable of interest y. I made sure that the Google series are highly correlated with y during the pre-...
0
votes
1answer
478 views
CausalImpact with Custom BSTS Model
I'm wanting to use a custom bsts model within the CausalImpact package, and I understand that I need to use a control group in my data; however, I don't really understand where that control group fits ...
