Clément de Chaisemartin
Biographic Data
| ID | 8759924 |
|---|---|
| NAME | Clément de Chaisemartin |
| GIVEN NAMES | Clément |
| FAMILY NAME | de Chaisemartin |
| SIGNATURE | DE CHAISEMARTIN C |
| AFFILIATIONS | Department of Economics, University of California at Santa Barbara, Santa Barbara, CA 93106 (e-mail: ) |
| VERIFIED | No |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 10 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Difference-in-Differences Estimators of Intertemporal Treatment Effects
We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for l periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commo…
Breaking the Barriers to Higher Education
The Direct and Spillover Effects of a Nationwide Socioemotional Learning Program for Disruptive Students
Social and emotional learning (SEL) programs that target disruptive students aim to improve their classroom behavior. Small-scale programs in high-income countries have demonstrated positive effects. Using a randomized experiment, we show that a nationwide SEL program in Chile has no effect. Very disruptive students seem to reduce the program’s effectiveness. With attention deficit hyperactivity disorder being more prevalent in middle- than high-…
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE ) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive. We propose another estimator that solves this issue. In the two applications we r…
Estimating the Effect of Treatments Allocated by Randomized Waiting Lists
Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent when the number of waitlis…
Estimating the Effect of Treatments Allocated by Randomized Waiting Lists
Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent when the number of waitlis…
Fuzzy Differences-in-Differences
Difference-in-differences (DID) is a method to evaluate the effect of a treatment. In its basic version, a “control group” is untreated at two dates, whereas a “treatment group” becomes fully treated at the second date. However, in many applications of the DID method, the treatment rate only increases more in the treatment group. In such fuzzy designs, a popular estimator of the treatment effect is the DID of the outcome divided by the DID of the…
Next Please! Estimating the Effect of Treatments Allocated by Randomized Waiting Lists
Fuzzy differences-in-differences
In many applications of the differences-in-differences (DID) method, the treatment increases more in the treatment group, but some units are also treated in the control group. In such fuzzy designs, a popular estimator of treatment effects is the DID of the outcome divided by the DID of the treatment, or OLS and 2SLS regressions with time and group fixed effects estimating weighted averages of this ratio across groups. We start by showing that wh…
Ready for Boarding? The Effects of a Boarding School for Disadvantaged Students
Boarding schools substitute school to home, but little is known on the effects this substitution produces on students. We present results of an experiment in which seats in a boarding school for disadvantaged students were randomly allocated. Boarders enjoy better studying conditions than control students. However, they start outperforming control students in mathematics only two years after admission, and this effect mostly comes from strong stu…
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Ready for Boarding? The Effects of a Boarding School for Disadvantaged Students
Boarding schools substitute school to home, but little is known on the effects this substitution produces on students. We present results of an experiment in which seats in a boarding school for disadvantaged students were randomly allocated. Boarders enjoy better studying conditions than control students. However, they start outperforming control students in mathematics only two years after admission, and this effect mostly comes from strong stu…
Fuzzy differences-in-differences
In many applications of the differences-in-differences (DID) method, the treatment increases more in the treatment group, but some units are also treated in the control group. In such fuzzy designs, a popular estimator of treatment effects is the DID of the outcome divided by the DID of the treatment, or OLS and 2SLS regressions with time and group fixed effects estimating weighted averages of this ratio across groups. We start by showing that wh…
Fuzzy Differences-in-Differences
Difference-in-differences (DID) is a method to evaluate the effect of a treatment. In its basic version, a “control group” is untreated at two dates, whereas a “treatment group” becomes fully treated at the second date. However, in many applications of the DID method, the treatment rate only increases more in the treatment group. In such fuzzy designs, a popular estimator of the treatment effect is the DID of the outcome divided by the DID of the…
Next Please! Estimating the Effect of Treatments Allocated by Randomized Waiting Lists
Estimating the Effect of Treatments Allocated by Randomized Waiting Lists
Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent when the number of waitlis…
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE ) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive. We propose another estimator that solves this issue. In the two applications we r…
Estimating the Effect of Treatments Allocated by Randomized Waiting Lists
Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent when the number of waitlis…
The Direct and Spillover Effects of a Nationwide Socioemotional Learning Program for Disruptive Students
Social and emotional learning (SEL) programs that target disruptive students aim to improve their classroom behavior. Small-scale programs in high-income countries have demonstrated positive effects. Using a randomized experiment, we show that a nationwide SEL program in Chile has no effect. Very disruptive students seem to reduce the program’s effectiveness. With attention deficit hyperactivity disorder being more prevalent in middle- than high-…
Breaking the Barriers to Higher Education
Difference-in-Differences Estimators of Intertemporal Treatment Effects
We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for l periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commo…
Advanced Causal Inference Techniques (6 works) · Computer Science (6 works) · Econometrics (6 works) · Economics (6 works) · Mathematics (6 works) · Statistics (6 works) · Estimator (5 works) · School Choice and Performance (5 works) · Average treatment effect (4 works) · Medicine (4 works)